Federated Learning for Decentralized Electricity Market Optimization: A Review and Research Agenda

Abstract

Decentralized electricity markets are increasingly shaped by the proliferation of distributed energy resources, the rise of prosumers, and growing demands for privacy-aware analytics. In this context, federated learning (FL) emerges as a promising paradigm that enables collaborative model training without centralized data aggregation. This review systematically explores the application of FL in energy systems, with particular attention to architectures, heterogeneity management, optimization tasks, and real-world use cases such as load forecasting, market bidding, congestion control, and predictive maintenance. The article critically examines evaluation practices, reproducibility issues, regulatory ambiguities, ethical implications, and interoperability barriers. It highlights the limitations of current benchmarking approaches and calls for domain-specific FL simulation environments. By mapping the intersection of technical design, market dynamics, and institutional constraints, the article formulates a pluralistic research agenda for scalable, fair, and secure FL deployments in modern electricity systems. This work positions FL not merely as a technical innovation but as a socio-technical intervention, requiring co-design across engineering, policy, and human factors.

1. Introduction

Decentralized electricity markets, once a theoretical construct of energy policy whitepapers, have become an operational reality in many grid systems worldwide. The rise of prosumerism, microgrids, and virtual power plants has fragmented the once-hierarchical structure of power generation and delivery into a dense mesh of actors—each with partial autonomy, limited observability, and conflicting incentives. This shift, driven not solely by technical innovation but also by socio-political imperatives toward decarbonization and energy justice, complicates coordination and control in ways that traditional optimization frameworks struggle to absorb.

At the heart of this transformation lies data—its proliferation, its fragmentation, and, not least, its sensitivity. Smart meters, DER controllers, EV charging platforms, and building management systems all generate torrents of temporal, spatial, and transactional data, much of which never leave its local node due to privacy constraints, regulatory red tape, or lack of economic incentives to share. In such an environment, learning global patterns from local data becomes both a necessity and a paradox.

Federated learning (FL) proposes [1] an inversion of the classical machine learning [2] workflow: instead of bringing the data to the model, it brings the model to the data—iteratively, asynchronously, and often under tight communication budgets. Initially popularized by the need to train predictive keyboards across millions of smartphones without uploading private messages to central servers, FL has since evolved into a broader paradigm for distributed model training across untrusted or semi-trusted clients. Its appeal to the energy domain is clear but deceptive.

Deploying FL in electricity systems introduces novel challenges that go well beyond data privacy. Energy data is temporally correlated, often sparse, and subject to real-world constraints imposed by physics (Kirchhoff’s laws do not negotiate) and market design (which may include price caps, ramping limits, or real-time penalties). Furthermore, clients in energy systems—whether substations, household batteries, or municipal aggregators—are not merely data holders but active agents with operational responsibilities. Their availability, reliability, and even willingness to participate in collaborative learning fluctuate over time.

And yet the promises are hard to ignore. Imagine a transnational demand response model that adapts to local grid topologies without sharing customer identities or usage logs. Or a federated reinforcement learning agent that fine-tunes voltage control policies across disparate distribution systems while preserving utility-specific constraints. Such visions are no longer mere blueprints on academic whiteboards. Several proof-of-concept studies, though limited in scope, suggest that FL can reconcile the tension between local autonomy and global coordination—if implemented with care.

This article does not claim to resolve the technical, regulatory, or organizational frictions that accompany this transition. Instead, it aims to synthesize current knowledge at the intersection of federated learning and energy market optimization. Our review covers foundational architectures, emerging use cases, reproducibility issues, and normative considerations. Rather than offering a singular trajectory for progress, we advocate for a pluralistic research agenda—one that accommodates the irreducible heterogeneity of real-world power systems and the disciplinary divides that continue to shape energy informatics.

Unlike previous reviews [1,2,3,4], this article not only surveys FL architectures but also integrates socio-technical aspects such as incentive compatibility, regulation, and ethics and provides a critical reflection on benchmarking and reproducibility.

To provide a unifying perspective, the overall workflow of federated learning in energy systems is illustrated in Figure 1. The diagram connects local model training at the client level with different aggregation strategies and highlights the main domains where FL has been applied. This schematic serves as a conceptual bridge between the theoretical underpinnings introduced in the next section and the application-focused analysis presented later in the article.

We begin by dissecting the theoretical roots of FL in Section 2, followed by an exploration of the architectures tailored for energy systems in Section 3. Section 4 discusses key application domains, from predictive modeling to market bidding strategies. Section 5 critically evaluates benchmarking practices, while Section 6 turns to ethical, regulatory, and interoperability concerns. We conclude with a proposed research agenda (Section 7) and final reflections (Section 8) on the pragmatic future of FL in decentralized energy ecosystems.

2. Theoretical Foundations and Taxonomies

The conceptual lineage of federated learning (FL) [1,3] is rooted in distributed optimization, though its emergence as a practical paradigm owes much to privacy-aware mobile computation [4]. The early formulation by McMahan et al. (2017) [5] in the context of language modeling across Android [5] devices was not merely a technical artifact—it was, in hindsight, a socio-technical pivot. It signaled a shift in how we think about learning systems: not as monolithic models hungrily devouring centralized data, but as orchestrated collectives of partial, local intelligences [6].

However, when transposed into the energy domain, this paradigm must confront a far less forgiving substrate. Electricity systems are not sandboxed apps. They are physical systems, governed by laws of conservation and stability, embedded in institutional layers, and riddled with path dependencies. The parameters of a power inverter or the dispatch routine of a CHP unit cannot be nudged without consequence. Every control action reverberates, not just through conductors and buses, but through contracts, tariffs, and regulatory bindings. Any learning process that ignores this coupling does so at its peril.

That is why naïvely importing FL from the domain of mobile NLP into energy systems yields brittle designs [7]. The nature of the data—temporal, sparse, event-driven, and reactive—complicates training dynamics [8]. Agents (e.g., transformers, DERs, and aggregators) do not merely hold data; they enact policies, enforce constraints, and modulate flows in real time [9]. Synchronizing such agents to participate in learning is non-trivial: some are intermittently connected; others are subject to legal limits on computation or communication [10].

To make sense of the diversity in implementations, a taxonomy becomes necessary—not to rigidly classify but to scaffold meaningful comparisons. We propose a tripartite framework based on heterogeneity, coordination architecture, and optimization scope.

2.1. Degree of Heterogeneity

Not all FL clients are created equal. In electricity networks, heterogeneity is the rule rather than the exception. Clients may vary in voltage levels (LV, MV, and HV), operational responsibilities (generation, storage, load), business models (regulated vs. deregulated), or even forecasting horizons (intraday vs. long-term planning) [11]. Statistical heterogeneity, often described in terms of non-IID data distributions, is just one facet. Systemic heterogeneity—where the objectives, risks, and update frequencies differ—is harder to quantify but more disruptive to convergence [12].

Some studies address this via personalized FL (pFedMe, Ditto, etc.), allowing clients to learn both a global model and local fine-tuned versions. Others introduce clustering layers—effectively learning federations within the federation [13]. However, the boundaries between local autonomy and global efficiency remain blurred, especially when conflicting signals emerge from local control policies and aggregated objectives.

2.2. Coordination Architectures

A second axis of variation lies in the coordination architecture. The canonical form, using a central aggregator, may work in pilot deployments or well-instrumented grids [14]. But centralization introduces a single point of failure—technical, economic, or political [15]. Furthermore, it fails to reflect the growing institutional push toward energy democratization and decentralization.

Hierarchical FL, as explored in multi-DSO or multi-region contexts, introduces intermediate aggregation nodes [16]. These may correspond to regional balancing authorities, microgrid controllers, or municipal platforms. The advantage lies in reduced communication costs and modular scalability. However, stale model updates and latency misalignment remain underexamined [17].

Fully decentralized architectures, sometimes borrowing from blockchain or gossip algorithms, offer resilience and privacy at the cost of higher complexity [18]. The challenge is not only technical—ensuring eventual consensus or model integrity—but also institutional. Who validates updates? Who bears the cost of inconsistency?

2.3. Optimization Scope

The third and final axis relates to what is being optimized. In energy systems, learning is often instrumental—used to inform control policies, market strategies, or asset schedules. The scope ranges widely:

(a)

Typical forecasting problems addressed with FL include short-term load prediction, photovoltaic (PV) output forecasting, and electricity price evolution [19].

Formal example (temporal correlation): Let L_t denote aggregate load at time t. A simple baseline that captures short- and daily-cycle dependence is Equation (1):

$$L_t=\alpha \hspace{0.17em}{L}_{t-1}+\beta \hspace{0.17em}{L}_{t-24}+{\gamma }^{\top }{X}_t+{\epsilon }_t$$

(1)

where X_t (e.g., temperature, calendar dummies) are exogenous covariates and ε_t is zero-mean noise. In FL, each client k estimates (α_k, β_k, γ_k) locally; aggregation yields (α⁻, β⁻, γ⁻) while personalization layers retain site-specific effects. This formalization clarifies why FL can outperform centralized training when heterogeneity is strongly local (e.g., occupancy-driven demand patterns).

(b)

Economic optimization tasks addressed with FL include the construction of bidding curves, portfolio allocation across diverse assets, and the development of hedging strategies to mitigate market risks.

Formal example (market clearing). At a stylized level, a static market clears at price p*, satisfying Equation (2):

$$\sum _i{D}_i\left(p^{*}\right)=\sum _j{S}_j\left(p^{*}\right)$$

(2)

In sequential settings, the operator updates pt such that $\sum _i{D}_i\left(p_t∣{\theta }_i\right)=\sum _j{S}_j(p_t\mid {\varphi }_j)$, subject to ramping and network constraints. Under FL, agents update local demand/supply parameters θ_i, ϕ_j using private data and share only gradients or parameters, providing a transparent baseline against which federated RL policies can be evaluated on welfare or profitability metrics.

(c)

Operational coordination tasks in FL include generation dispatch, renewable curtailment management, and the activation of reserves to ensure grid reliability [20].

Formal example (network constraints under DC approximation): Physical feasibility can be expressed as P = Bθ, with branch flow on line ℓ between buses i and j given by Equation (3).

$$F_{\mathcal{l}}={\displaystyle \frac{{\theta }_i-{\theta }_j}{X_{\mathcal{l}}}}$$

(3)

Nodal balance $AF+P^{inj}=0$, and limits $\left|F_{\mathcal{l}}\right|\le \overline{F_{\mathcal{l}}},\theta \in [\underset{\_}{\theta },\overline{\theta }]$: Hybrid FL schemes can incorporate these constraints via a local feasibility-projection step Π_F(·) after each client update, blending the interpretability of constrained optimization with the flexibility of deep learning while keeping raw grid data local.

Some works attempt multi-task FL [21], learning across multiple outputs simultaneously. Others focus narrowly on one task, often for benchmarking. A key insight, however, is that optimization in energy markets is rarely an end in itself. It is nested within broader institutional and physical frameworks. A model that minimizes RMSE may still violate thermal ramp constraints or generate economically infeasible schedules.

2.4. Relation to Classical Distributed Optimization

It would be remiss not to contrast FL with its older cousin: distributed optimization. Techniques such as ADMM [22,23], dual decomposition [24], or primal-dual methods have long been used in power system control. What distinguishes FL is the decoupling of model architecture from constraint handling [25]. Rather than solving optimization problems across agents with explicit coupling constraints, FL focuses on parameter consensus through repeated local updates and global aggregation.

In practice, the boundary is porous. Several recent hybrid schemes reintroduce constraint-enforcing steps or local feasibility projections into FL rounds, blending the interpretability of distributed optimization with the flexibility of deep learning. This hybridity may well be the direction forward—particularly in domains, like electricity [26], where feasibility is non-negotiable.

A diverse set of algorithms has been explored in federated energy applications, each with distinct computational and operational trade-offs. In forecasting tasks, recurrent architectures such as LSTMs and GRUs remain dominant due to their ability to capture temporal dependencies in consumption or generation data. While these models often deliver high predictive accuracy, their training cost can be considerable in federated settings, as gradient exchange and synchronization amplify communication overhead. Lightweight alternatives such as CNN-LSTM hybrids reduce complexity but may sacrifice interpretability and long-horizon accuracy.

For strategic decision-making tasks such as bidding or dispatch optimization, reinforcement learning approaches—including DDPG, PPO, and actor–critic variants—have gained traction. DDPG offers fine-grained policy learning in continuous action spaces, making it suitable for dynamic pricing or frequency control; however, its sensitivity to hyperparameters and exploration noise often complicates convergence in heterogeneous federated environments. PPO, by contrast, tends to exhibit more stable training, tolerating delayed or asynchronous updates more gracefully, though at the cost of higher sample complexity. Actor–critic methods strike a balance, but their computational footprint can pose challenges for resource-constrained clients at the grid edge.

In anomaly detection and predictive maintenance, unsupervised methods such as autoencoders, one-class SVMs, or isolation forests are favored for their ability to model rare events without extensive labeled data. Autoencoders, when federated, achieve broad generalization across heterogeneous sensor deployments, yet they require careful regularization to prevent reconstruction bias toward dominant clients. SVM-based methods impose lower computational demands but scale poorly with very large, distributed datasets, while ensemble approaches like isolation forests offer robustness against noise at the cost of interpretability.

The literature suggests no universal algorithmic winner. Instead, the choice of model reflects a trade-off between predictive fidelity, communication cost, and client capability. Computational complexity becomes particularly salient in FL, where the burden of local training and communication must be balanced against the heterogeneity of both the data and the hardware infrastructure.

These formal anchors (temporal dependence, market clearing, and network feasibility) motivate architectural choices discussed next, where coordination and synchronization determine how such constraints are respected at scale.

3. Federated Learning Architectures in Power Systems

No single architecture suits all deployments. Federated learning, as a paradigm, offers a malleable envelope—within it, one must negotiate trade-offs: scalability vs. precision, latency vs. convergence, centralization vs. resilience [27,28]. In energy systems, these trade-offs are not abstract. They translate into voltage stability violations, missed balancing markets, or uneconomical dispatch decisions [29]. Hence, architectural design is not merely a technical exercise, but a systems-level commitment.

Below, we delineate four dominant FL architectures observed in power system literature and pilots, alongside their operational assumptions and systemic limitations [30].

3.1. Centralized Aggregation: The Classical FedAvg Lineage

The most common architecture in early energy-related FL deployments is a direct adaptation of FedAvg [5]: a central server initializes the model, dispatches it to all clients, aggregates their locally updated parameters (often via weighted averaging), and redistributes the global model [31]. Figure 2 illustrates the canonical centralized FL system, where a single aggregator coordinates all client updates. This architecture is widely used but suffers from single-point failures (Figure 2). In this architecture, a central server is responsible for initializing the global model, distributing it to multiple clients, and aggregating the locally trained updates into a unified version. Each client performs training on its private dataset (e.g., Client 1 on Data A, Client 2 on Data B, and Client 3 on Data C), ensuring that sensitive data remains decentralized while only model parameters or gradients are exchanged. The central server then combines these updates—typically through weighted averaging—and redistributes the improved global model back to all participants. Although simple and widely adopted in early energy-related FL studies, this configuration has notable drawbacks: the central node introduces a single point of failure, creates potential bottlenecks in communication, and requires a relatively high level of trust from all participating entities. Nevertheless, it remains a baseline reference for understanding how federated learning can be adapted to energy systems and serves as a foundation for exploring more advanced architectures such as hierarchical or peer-to-peer FL.

Its popularity stems from its simplicity. Researchers can control convergence properties, introduce fairness constraints, or plug in dropout-tolerant updates. Simulation environments such as PySyft v0.9.5 [32] or Flower v2.0.1 [33] offer out-of-the-box support for such settings. But energy systems are not controlled labs. Nodes may fail, data may arrive asynchronously [34], or bandwidth might be constrained. In such settings, centralized FL architectures become brittle [35].

In particular, centralized schemes assume a level of trust and connectivity that often does not exist. A municipal DSO may not want to share model gradients with a national TSO, even if no raw data is involved. Furthermore, central servers become natural bottlenecks in terms of both communication and influence.

3.2. Hierarchical Federated Learning: Structuring the Grid as a Learning Tree

Recognizing the layered structure of energy systems—where household prosumers, local aggregators, regional dispatch centers, and national regulators interact—several researchers have proposed hierarchical FL [36,37] as a more natural mapping. In such architectures, regional agents act as sub-aggregators, collecting model updates from subordinate clients (e.g., smart homes [38] or EV stations [39]), aggregating them locally, and then passing a distilled version upward.

In practice, hierarchical FL architectures in power systems are tightly linked to the existing measurement infrastructure. Phasor Measurement Units (PMUs) provide time-synchronized data on voltage and current phasors with high accuracy, and Phasor Data Concentrators (PDCs) aggregate these measurements from multiple PMUs. Traditionally, PDCs have been deployed centrally, but modern smart grids increasingly adopt distributed or edge-based PDCs to reduce latency and improve bandwidth utilization. Within an FL setting, regional PDCs can naturally function as sub-aggregators: they consolidate local updates, ensure data synchronization, and forward distilled model parameters upward in the hierarchy. This integration anchors FL architectures in the physical reality of grid monitoring.

Hierarchical FL (HFL) reduces communication overhead and may enhance scalability [40,41], particularly when model updates remain within jurisdictional or organizational boundaries. Hierarchical FL also permits different update frequencies. For example, local nodes may perform hourly training, while regional aggregators synchronize daily or weekly to balance accuracy with communication efficiency (Figure 3) [42]. In HFL, local clients perform training on private data and share model updates with edge aggregators, which in turn coordinate with a global server. This structure reduces communication cost and aligns with the hierarchical control layers of modern grids (Figure 3).

Yet this architecture raises new questions: What if updates at lower levels are conflicting? How does one balance model staleness with communication cost? And—importantly—how does one integrate control feedback loops, where a regional controller not only aggregates data but adjusts setpoints, effectively altering the data-generating process? These are not yet resolved in the current literature.

3.3. Peer-to-Peer and Gossip-Based FL: Architectures Without a Center

For truly decentralized energy environments—off-grid systems, energy cooperatives, or blockchain-mediated energy trading—centralized or even hierarchical models are ideologically and operationally unsuitable. Here, gossip-based FL or peer-to-peer architectures offer an alternative [43,44,45,46].

In gossip FL, nodes exchange model updates with a subset of peers, using consensus protocols or weighted aggregation based on trust scores or topological proximity. These systems are naturally resilient to single-point failures [47] and can self-organize [48]. They also align well with energy communities [49], where trust is locally defined and data governance remains distributed.

However, the engineering burden is high. Routing logic, update ordering, loop detection, and security primitives (e.g., Sybil attack prevention [50]) must be explicitly designed. Moreover, convergence is slow unless the gossip protocol is tuned. In energy systems, where response time matters, this lag can be prohibitive.

Few real-world studies exist that apply gossip FL in grid settings. One exception is Zhang & He (2022) [51], who simulate a blockchain-backed gossip FL for EV charging coordination, achieving robustness under node dropout. Still, these remain sandbox demonstrations.

3.4. Blockchain-Enabled FL and Smart Contract Aggregation

A hybrid approach—still largely theoretical but garnering attention—is to combine FL with distributed ledger technology (DLT) [52]. In such settings, model updates are submitted as transactions to a blockchain [53], verified via consensus, and stored immutably. Smart contracts govern the aggregation logic, update permissions, and even incentive payments [54].

In principle, this architecture offers auditability [55], resistance to tampering asante, and automatic enforcement of participation rules [56]. For example, clients could be rewarded in tokens for high-quality updates or penalized for model poisoning attempts [57].

But the computational and communication costs of DLT are non-negligible. Public blockchains (e.g., Ethereum) are too slow [58], and private ones (e.g., Hyperledger) require governance frameworks [59,60]. Moreover, the use of blockchain introduces a misalignment: FL thrives on minimizing communication, while DLTs traditionally duplicate information for consensus [61].

Despite this, projects like ChainFL [62] and Energy Web Foundation [63] are exploring such integration—especially in contexts where trust is minimal and transparency is paramount (e.g., transnational balancing markets).

3.5. Event-Driven and Asynchronous FL

Finally, an important but often neglected category is asynchronous FL [64], where clients update at different intervals [65], based on local events (e.g., demand spikes and fault detection) rather than fixed global rounds. In energy systems, this is not a luxury but a necessity.

Asynchrony introduces challenges in aggregation: some nodes may contribute outdated models, others may dominate learning due to higher frequency. Techniques such as staleness weighting [66], momentum updates [67], or adaptive learning rates [3] have been proposed. However, few have been validated under real grid conditions.

Event-driven FL [68], where learning is triggered by domain-specific signals (e.g., over-frequency events, market price deviations), could align FL with operational priorities. Yet this requires tight coupling between control systems and learning agents—a co-design still rarely seen in practice.

In power systems, asynchronous updates are often event-driven rather than strictly time-driven. For example, model synchronization may be triggered only when demand changes exceed a threshold, when a generator failure occurs, or when renewable output sharply deviates from forecasted levels. Such event-driven FL reduces unnecessary communication, aligns naturally with the non-stationary character of energy systems, and supports scalability when thousands of clients participate.

4. Application Domains in Energy Markets

Federated learning in power systems [69,70] is not merely a technical endeavor—it is a response to the structural fragmentation of modern electricity markets. Unlike vertically integrated grids of the past, today’s markets host a polyphony of actors: households with rooftop PV, aggregators managing hundreds of behind-the-meter batteries, municipal DSOs, and high-frequency traders operating in balancing markets. Each actor has data, but no one has all of it. The case for FL arises not out of convenience but out of necessity (Figure 1).

In this section, we explore key application domains where FL has been trialed, theorized, or proposed as a strategic advantage. Some use cases are extensions of centralized ML tasks (e.g., load forecasting); others are novel, made possible only by the distributed nature of FL.

4.1. Load Forecasting and Demand Curve Estimation

The poster child of energy AI remains short-term load forecasting. Nearly every utility runs some variant of it. Traditionally, forecasting was centralized [71]: grid operators collected smart meter data, weather predictions [72,73], and historical usage, (LSTM, and trained ensemble models [74] or recurrent neural networks such as GRUs [75]).

But the centralization assumption is faltering. In urban environments with millions of meters, privacy constraints and bandwidth saturation hinder raw data transmission. In FL-based approaches, each smart meter—or, more realistically, each building controller—trains a local model on its consumption patterns and shares gradients or parameters [76,77].

Studies [69,78] demonstrate that federated LSTM networks can match or even exceed centralized models in accuracy, especially when data is highly localized (e.g., buildings with unique occupancy schedules) [79]. Personalization layers—local fine-tuning atop a global base—have shown particular efficacy [80].

Yet FL in forecasting remains relatively conservative. Models are trained to minimize RMSE or MAE [81], rarely aligning with market needs: predicting peak load timing, for example, matters more than minimizing overall error. Moreover, few studies explore multi-horizon forecasting, where the model simultaneously predicts 15 min, hourly, and day-ahead values—each relevant for different market mechanisms.

Recent empirical studies underscore the competitive edge of federated learning in load forecasting. For example, Petrangeli et al. (2022) [34] demonstrate that FL configurations such as FedAvg and FedSGD can offer performance comparable to—or even slightly better than—centralized models while maintaining data privacy. Similarly, Briggs et al. (2022) [82] show that federated learning enhanced with hierarchical clustering improves convergence rates significantly under non-IID conditions, reducing the communication rounds needed to reach target accuracy.

4.2. Federated Reinforcement Learning for Bidding and Pricing

Perhaps the most tantalizing application—though still speculative in many respects—is federated reinforcement learning (FRL) for bidding strategies [83]. In deregulated markets, generators and flexible loads submit bids to day-ahead or intra-day markets, often adjusting behavior in real-time based on price signals and grid constraints [84,85].

In theory, reinforcement learning (RL) is well-suited to this: agents learn optimal policies by interacting with an environment, adjusting actions to maximize long-term rewards [86,87]. In practice, training such agents centrally is both privacy-invasive and commercially unacceptable—no aggregator will upload its strategic behavior to a shared server [88].

Federated RL offers a path forward. Each agent trains its local policy—often via DDPG [89], PPO [90], or actor–critic variants [91]—using private operational data and market feedback. Periodically, policy parameters or Q-functions are aggregated to inform a shared policy. The goal is not to enforce uniformity, but to accelerate learning across agents facing similar conditions.

Early results suggest that FRL can reduce convergence time and improve profit margins in simulations [92]. However, stability remains a concern: energy markets are non-stationary, with sudden price spikes or regulatory shifts. A federated policy may lag behind, reacting sluggishly or even overfitting to stale dynamics.

Moreover, incentive compatibility is not guaranteed. Agents may choose to deviate from federated updates if local profits improve. This introduces a layer of game theory atop learning theory, still largely unexplored in current literature.

4.3. Real-Time Balancing and Ancillary Service Coordination

Balancing supply and demand in real-time requires coordination across dozens or hundreds of distributed assets [93,94]. Batteries, flywheels, and flexible HVAC systems can each respond to frequency deviations or ramping needs but must do so within physical and contractual limits [95].

Traditionally, this domain has been served by direct control: TSOs send signals, and assets react. But this model scales poorly and requires tight integration.

FL enables a new mode: local agents learn control policies based on local frequency, voltage, and grid conditions [82], while periodically synchronizing models to align with system-level goals [96]. This is particularly attractive in ancillary service markets, where small resources can aggregate into virtual power plants (VPPs) and provide services such as spinning reserve or voltage support [97].

Several challenges persist. First, the learning objective is not just to follow grid signals but to optimize economic return under physical constraints. Second, latency matters: federated updates must occur within operational windows, which can be seconds in some cases. Lastly, coordination must respect regulatory boundaries—e.g., only licensed aggregators may offer services in certain jurisdictions.

4.4. Grid Congestion and Voltage Control with FL

As distributed generation increases, congestion in medium- and low-voltage networks becomes more frequent. Overvoltage due to solar PV in midday hours and transformer overloading from simultaneous EV charging are now real operational concerns [98].

FL has been proposed as a mechanism for distributed voltage control: each inverter learns a local reactive power control policy, taking into account voltage readings and local grid impedance [91]. Periodically, these policies are federated to harmonize system-wide behavior without requiring full topology disclosure [99].

Here, physics meets ML in a delicate balance. Control policies must remain stable, respect bounds, and avoid instability loops. Approaches like safe RL or constrained optimization layers have been embedded into FL frameworks with some success. Still, few of these have moved beyond simulation [100].

In voltage control, federated reinforcement learning has been tested in simulation environments where distributed controllers train policies locally and periodically share model updates with a global aggregator. Published studies show that such federated schemes are capable of maintaining stability margins and voltage profiles comparable to centralized safe RL controllers. At the same time, FL allows each utility or operator to retain full control over sensitive grid-level operational data, reducing concerns about data exposure while still contributing to a collective optimization process.

The effectiveness of federated voltage control also depends on reliable system observability. Wide-area monitoring systems (WAMSs), built on PMUs and distributed PDCs, provide the synchronized state knowledge required to coordinate local controllers. Incorporating FL into this infrastructure ensures that local inverters and controllers can adapt policies based on accurate, real-time phasor data, while the federated framework preserves confidentiality and avoids the need for disclosing the full network topology.

4.5. Predictive Maintenance and Asset Health Monitoring

One of the more mature—but less publicized—applications is in predictive maintenance [101]. Transformers, circuit breakers, and underground cables all generate logs, vibration patterns, and partial discharge data. Sharing this data centrally poses cybersecurity and privacy risks.

Federated anomaly detection—using autoencoders [102,103], one-class SVMs [104], or isolation forests [105]—has been used to detect incipient failures without centralizing sensitive sensor data. In many cases, models are trained on edge gateways installed in substations or control cabinets Schneible.

This application is relatively low-risk (offline training, non-real-time) and thus often serves as an entry point for FL adoption in grid operators.

The main application domains of federated learning in power systems, together with representative models, architectural choices, and key challenges reported in the literature, are summarized in

Table 1. Example use cases of federated learning in power systems.

Use Case	Typical Models	FL Architecture	Key Challenges	Search Keywords
Load Forecasting	LSTM, GRU, CNN-LSTM hybrids	Centralized or Hierarchical	Data sparsity, personalization, multi-horizon alignment	federated learning load forecasting energy LSTM smart grid
Federated RL for Bidding	DDPG, PPO, Actor-Critic RL	Peer-to-Peer, Hierarchical	Strategic behavior, non-stationarity, policy lag	federated reinforcement learning electricity market bidding PPO DDPG
Balancing and Ancillary Services	Feedforward DNN, Safe RL, MPC + FL	Hierarchical, Event-Driven	Latency, physical constraints, regulatory compatibility	federated learning ancillary services grid balancing smart inverter
Voltage Control and Congestion Management	Safe RL, Q-learning, Constraint-aware DNNs	Hierarchical, Blockchain-enabled	Grid stability, topology opacity, multi-agent coordination	federated learning voltage control congestion smart grid reactive power
Predictive Maintenance	Autoencoders, One-Class SVM, Isolation Forest	Centralized (Edge Aggregation)	Sensor heterogeneity, privacy, communication efficiency	federated learning predictive maintenance substations anomaly detection

.

While the majority of studies emphasize the conceptual potential of FL in decentralized electricity markets, a growing body of work has begun to quantify its advantages over centralized approaches. For example, in short-term load forecasting, several comparative studies demonstrate that federated LSTM or GRU networks can achieve accuracy on par with, or in some cases exceeding, centralized models. This is particularly evident when data distributions are highly localized: federated models maintain lower RMSE in heterogeneous residential datasets where centralized approaches struggle to capture regional variability. In contrast, when data is more homogeneous, the performance gap narrows, suggesting that FL’s main benefit lies in preserving privacy and ensuring scalability rather than improving baseline accuracy.

In bidding and pricing strategies, federated reinforcement learning has been shown to accelerate convergence relative to isolated local learners while avoiding the strategic opacity of centralized policy training. Case studies in day-ahead markets indicate that FL-trained agents can maintain profitability comparable to centralized RL benchmarks, with the added advantage of preserving the confidentiality of bidding data. Importantly, federated coordination mitigates instability caused by non-stationary market dynamics, although the margin of improvement depends strongly on the synchronization scheme employed.

Similarly, in voltage control and congestion management, federated policies typically match the performance of centralized safe RL controllers in terms of maintaining stability margins but offer distinct institutional benefits: utilities and aggregators can retain control over proprietary network data while still contributing to a global policy. Even in predictive maintenance, where privacy concerns are less acute, federated anomaly detection provides resilience advantages, ensuring that failure signatures are learned across diverse environments without transferring raw sensor data.

Taken together, these findings suggest that the “value-add” of FL is not universally higher predictive accuracy but rather robust performance under heterogeneity, stronger privacy guarantees, and enhanced institutional acceptability compared to centralized benchmarks.

5. Evaluation Practices and Reproducibility

For a field so entangled with optimization, one might expect the evaluation of federated learning in energy systems to be—if not standardized—then at least coherent. It is not.

A persistent limitation in the current literature is the lack of standardized evaluation protocols, which complicates meaningful comparison between federated and centralized approaches. Forecasting studies most often report RMSE or MAE yet rarely consider metrics that are more directly aligned with market or operational objectives, such as peak load prediction accuracy, imbalance penalties, or reserve activation rates. Similarly, reinforcement-learning-based bidding frameworks typically evaluate cumulative reward but seldom benchmark against economic outcomes observed under centralized RL or traditional optimization methods. This disconnect makes it difficult to quantify the true performance gains of FL beyond generic predictive accuracy.

Another challenge lies in the treatment of computational efficiency. While many studies highlight privacy as a key benefit, few assess the added training time, communication overhead, or energy consumption associated with federated orchestration. These dimensions are crucial for deployment in real grid environments, where edge devices may operate under strict resource constraints. Comparative analyses that explicitly measure both model fidelity and computational cost would provide a more balanced view of FL’s trade-offs relative to centralized baselines.

Equally important is the need for reproducibility. Code availability remains the exception rather than the norm, and when implementations are shared, preprocessing pipelines and simulation details are often omitted. As a result, even claims of modest improvements cannot easily be validated or extended. Establishing open, domain-specific benchmarking environments—integrating realistic load profiles, market dynamics, and network constraints—would allow the community to test algorithms under consistent conditions. Only through such harmonized evaluation frameworks can the benefits of FL be objectively demonstrated and fairly weighed against conventional centralized methods.

The literature reveals a mosaic of metrics, experimental setups, and assumptions that often defy comparison. Worse, these discrepancies are rarely acknowledged. One study may proclaim the superiority of federated models over local baselines; another may celebrate privacy preservation without quantifying its cost to model fidelity. And yet both may be published in the same venue, without so much as a shared benchmark.

Let us begin with the datasets—or the lack thereof. A large fraction of FL studies in energy domains rely on synthetic data [106], often generated from simulators [107] or time series bootstraps. This is understandable: real smart meter data is rarely accessible due to privacy regulations, and commercial bidding logs are considered proprietary [108]. But synthetic data is a double-edged sword. It enables rapid prototyping, yes, but it also detaches the model from the operational realities of the grid. Load patterns lack behavioral diversity, weather traces are overly smooth, and outliers are rare or non-existent. As a result, reported gains may collapse when faced with live deployments.

Some open datasets exist—the UCI Individual Household dataset [109], Pecan Street [110], and the UK-DALE [111] series—but even these have limitations in scope, granularity, or geographical diversity. Few include event annotations or economic variables such as dynamic pricing, imbalance costs, or grid constraints. None offer native support for federated learning evaluation. Consequently, researchers must simulate federation by slicing data into artificial clients—by household, by region, or randomly. These splits introduce biases that go largely unexamined.

On the metrics side, the situation is equally fragmented. In forecasting tasks, the canonical RMSE and MAPE [112] dominate, despite their well-known limitations in skewed or sparse distributions. In reinforcement-learning-based bidding or control scenarios, cumulative reward is often used without a clear linkage to real market outcomes. Rarely do papers report economic metrics: profit margins, market participation rates, and penalty costs. Rarer still are grid reliability indicators, such as frequency violations, transformer overloads, or reactive power excursions.

Moreover, the privacy–performance trade-off—the ostensible raison d’être of FL—is often discussed qualitatively, if at all. Some works mention differential privacy, while others propose homomorphic encryption [113] or secure aggregation [114], but almost none quantify the loss in accuracy due to these safeguards. The implicit assumption seems to be that privacy is a binary switch: you either apply a mechanism or not. But in practice, noise budgets and encryption overheads form a continuum, and we lack a vocabulary—let alone a methodology—for navigating it.

Reproducibility suffers from a different set of ailments. Many papers do not publish code. When code is available, it often omits data preprocessing scripts, simulation environments, or configuration files. Hyperparameters—particularly for FL-specific settings like number of clients Agrawal, client sampling ratio, communication rounds, or aggregation frequency—are inconsistently reported [115,116]. Some works conflate local training epochs with global rounds. Others omit the number of participating clients entirely.

There are a few exceptions. Frameworks like FedML [117], Flower [33], and TensorFlow [118] Federated have begun to establish experimental baselines, and some energy-oriented libraries (e.g., OpenDSS [119], GridLAB-D [120]) have been adapted to simulate decentralized settings. However, integration between physical system simulation and FL orchestration remains nascent.

What is urgently needed is not another leaderboard, but a modular, domain-aware evaluation framework—something akin to OpenAI Gym, but with power flow constraints, asset topologies, and market clearing logic baked in. Such a framework would allow researchers to plug in models, define privacy budgets, simulate realistic data availability, and benchmark under common assumptions.

Finally, there is the epistemological question of what evaluation should mean in federated energy systems. Is the goal to outperform centralized models? To protect privacy with minimal performance degradation? To maximize participation rates among prosumers? Each implies different experimental priorities. Without clarity on these priorities, progress risks becoming performative: a parade of models, each slightly more complex, evaluated on incompatible grounds.

6. Regulatory, Ethical, and Interoperability Aspects

Federated learning (FL) is often introduced to the energy domain under the banner of privacy: no raw data leaves the node, no central collector sees your consumption profile, and yet, a system-wide model emerges. It is a compelling narrative—but one that, upon closer inspection, reveals cracks (

Table 2. Key aspects of federated learning usage in power systems.

Dimension	Current State	Challenges	Urgency Level
Privacy Compliance	Partially addressed (DP, secure aggregation); legal ambiguity remains	Gradient leakage, legal definitions of personal data, trade-offs with accuracy	High
Fairness and Bias	Largely ignored; no fairness metrics or adjustments in FL updates	Skewed representation, asynchronous bias, economic marginalization	Medium–High
Interoperability	Low; device and protocol heterogeneity hinder model exchange	Lack of standards (APIs, model formats), legacy systems, model alignment	High
Incentive Mechanisms	Theoretical only; no standard reward or reputation mechanisms	Free-riding, model poisoning, cost of compute, lack of trust incentives	Medium

).

6.1. Privacy Regulations and Their Ambiguities

The General Data Protection Regulation (GDPR) [121], the California Consumer Privacy Act (CCPA), and similar legislative frameworks are frequently cited as motivations for FL adoption in electricity systems. However, these regulations were not written with federated optimization in mind. They concern data ownership, consent, and identifiability—not the statistical properties of gradient updates.

Federated learning may reduce direct data transfer, but it is not inherently compliant. In fact, model updates can leak information, particularly in small datasets or when updates are infrequent. Recent adversarial attacks demonstrate that under certain conditions [122], it is possible to reconstruct input features—or even user identities—from model gradients.

In response, techniques such as differential privacy (DP) [123], secure aggregation, and homomorphic encryption have been proposed. Yet these solutions introduce a trilemma: privacy, accuracy, and efficiency cannot all be maximized simultaneously. Differential privacy, for instance, degrades model performance as noise is added [124]. Homomorphic encryption increases computational overhead [125]. Secure aggregation requires additional communication rounds and trusted setup phases.

None of these tensions are resolved in the current regulatory environment. Worse, regulators lack clear guidance on whether FL constitutes data processing, whether model updates are personal data, or who is responsible for violations in a decentralized learning collective. These gray zones hinder adoption by risk-averse utilities and grid operators.

6.2. Ethical Considerations Beyond Privacy

The ethical implications of FL extend well beyond data protection [126]. Bias is a critical but understudied dimension. If clients vary widely in terms of data richness (e.g., urban vs. rural prosumers), then the global model may be skewed toward overrepresented regions. Similarly, early participants in training may unduly influence the model trajectory—especially in asynchronous FL setups.

Furthermore, there is the risk of economic exclusion. In many schemes, clients who contribute more data or computational resources receive greater benefits (e.g., better personalization, higher incentives). While this is rational from a game-theoretic perspective, it may entrench inequalities [127]. Aggregators in resource-rich areas may optimize better, trade better, and accumulate disproportionate market power.

To date, no major FL deployment in energy includes explicit fairness mechanisms, such as re-weighting updates from underrepresented nodes or calibrating incentives to promote equitable participation. Nor is there consensus on how to measure fairness: by geography, consumption level, or access to infrastructure?

6.3. Interoperability: The Forgotten Constraint

The promise of FL rests on a deceptively simple assumption: that clients can run compatible models and exchange updates using shared protocols. In practice, interoperability is a structural bottleneck [128].

Smart inverters from different manufacturers do not expose standardized APIs. Energy management systems use proprietary file formats and configurations. Aggregators, TSOs, and DSOs operate under distinct software stacks, often built over decades. Without shared standards, implementing even basic FL synchronization becomes an engineering nightmare.

There are emerging standards, such as the IEEE 3652.1-2020 draft standard for federated machine learning, which provide guidance on architectural and protocol layers [129], and extensions to IEC 61850 enabling distributed intelligence, allowing peer IEDs to autonomously recognize network topology and configure data flows [130]. However, adoption has been slow, and many utilities remain unaware of these initiatives.

Worse, model interoperability is rarely addressed. FL assumes that models are structurally identical across clients—a reasonable assumption in mobile apps, where every phone runs the same codebase, but not in energy systems. A household may have a shallow GRU; a regional aggregator may run a transformer-based architecture. Aligning these under a common gradient or parameter space remains an open challenge.

6.4. Incentive Mechanisms and Participation Dilemmas

Participation in federated learning is not free. Clients must allocate compute cycles, energy, and—perhaps most valuable—trust [131]. Why should a household energy management system engage in collaborative learning, especially if it cannot observe the benefits?

In financial services, federated learning frameworks like FATE and OpenMined have explored token-based incentives or reputation scores [132]. In energy, no such systems exist in production. A few simulation studies propose credit systems, where participants receive proportional returns based on data quality or model contribution. But this raises further questions: Who audits the quality? Who administers the credits?

There is also the risk of free-riding or model poisoning. An adversarial client could submit manipulated updates to skew the model toward its own bidding advantage—or simply participate to observe aggregated updates and reverse-engineer market patterns.

Mitigating such behaviors requires cryptographic safeguards, reputational enforcement, or contractual arrangements—all of which introduce overheads and complexity. At present, the social infrastructure of FL in energy remains aspirational (Table 2).

6.5. Policy and Regulatory Framework

Federated learning (FL) is increasingly relevant in modern energy systems, and its adoption intersects with several evolving policy and regulatory frameworks. In the European Union, the Electricity Market Design reforms (notably Directive 2019/944 and the May 2024 amendments under the Clean Energy for All Europeans package) promote enhanced market flexibility, active consumer participation, and cross-border integration—areas where FL can enable distributed forecasting, demand response optimization, and local flexibility while upholding data privacy and stakeholder autonomy [133].

At the cybersecurity level, the NIS2 Directive (Directive (EU) 2022/2555) mandates stringent risk management, incident reporting, and supply-chain resilience for entities within the energy sector—including DSOs, TSOs, aggregators, and market operators [134]. FL frameworks, by design, minimize central data exposure and inherently support these cybersecurity principles, reducing attack surfaces and enhancing decentralization. Nonetheless, sector stakeholders must ensure FL deployments align with NIS2 requirements such as secure logging, system monitoring, and incident response governance across federated nodes.

Moreover, compliance with ISO/IEC 27001 standards [135] for information security management systems and awareness of cyber-resilience directives such as the Cyber Resilience Act (CRA) can further strengthen trust in FL solutions by institutionalizing encryption, vulnerability management, and secure development lifecycle practices [136].

Together, these regulatory frameworks establish a foundation for deploying FL in energy systems in a manner that supports market integration, protects critical infrastructure, and fosters cyber-resilience—though current adoption remains nascent, and more targeted guidance would accelerate uptake.

7. Research Agenda and Open Challenges

It is tempting to believe that federated learning (FL) in power systems is merely a matter of scale and patience—that the models will mature, the hardware will catch up, and the regulatory frameworks will adapt. But this belief ignores the deeper frictions at play. FL is not a plug-and-play component; it is a systems-level intervention. And systems resist simplification.

To move beyond proof-of-concept deployments and towards practical, resilient FL systems in electricity markets, we identify a set of open research questions. These are not exhaustive, nor necessarily sequential. But they reflect the multifaceted nature of the challenge—technical, organizational, and ethical (Figure 3,

Table 3. Open Research Areas, Challenges, and Urgency in Federated Learning for Power Systems.

Research Area	Key Questions/Needs	Open Challenges	Urgency
Convergence under Grid Conditions	Formalize behavior under heterogeneity, asynchrony, staleness	No general theory under non-IID, dynamic nodes	High
Federated RL for Market Dynamics	Coordinate strategic agents; link policies to market equilibria	Lack of theory at RL–FL–game theory intersection	Medium–High
FL and Grid Co-Simulation	Simulate full loop with control systems and power flows	No existing end-to-end FL + grid simulation pipeline	High
Incentive-Compatible Protocols	Design reward systems for honest and useful participation	Lack of auditability, fairness, or verifiable rewards	Medium
Benchmarking Environments	Create realistic federated datasets and environments	No standard scenarios for federated benchmarking	High
Human-in-the-Loop FL	Enable explainability and operator override of FL systems	Explainability methods not adapted for FL aggregation	Medium–High

).

7.1. Formal Convergence Under Real Grid Conditions

Most convergence guarantees in FL assume IID data, synchronous updates, and homogeneous model structures. These assumptions are systematically violated in power systems. Clients differ in temporal patterns (residential vs. industrial loads), in compute capability (edge gateways vs. cloud backends), and in update frequency (real-time controllers vs. day-ahead planners).

There is a pressing need for formal models that describe FL behavior under time-varying, asynchronous, and structurally heterogeneous conditions. This includes bounds on staleness, quantification of update drift, and convergence rates in multi-level hierarchies. Without these, deployment remains an act of faith.

7.2. Federated Reinforcement Learning for Market Dynamics

Energy markets are dynamic games, not static functions. Prices fluctuate, rules change, and actors adapt. Federated reinforcement learning (FRL) offers a pathway to decentralize policy learning, but its theoretical underpinnings in this domain are thin [131].

Key questions include the following: How can learning be coordinated across strategic agents with partially aligned incentives? Can local policies converge to an approximate market equilibrium? What are the implications of sharing Q-functions across competitors? The intersection of game theory, RL, and FL remains largely unmapped.

7.3. Co-Simulation of FL with Grid Control Systems

Too often, FL experiments are isolated from the physical grid. Models are trained on load curves or market traces, but the downstream effects—on voltages, line congestion, system frequency—are ignored.

A high-impact research direction involves the co-simulation of federated learning with power system simulators such as GridLAB-D [137], OpenDSS [138], or PowerFactory [139]. This would enable end-to-end evaluation of learning policies on actual grid dynamics. More ambitiously, we envision closed-loop FL, where control signals are generated by the evolving model and directly influence the environment.

7.4. Incentive-Compatible Protocols for Participation

As discussed in Section 6, participation in FL is not free. Clients expend computing, energy, and organizational bandwidth. Incentives matter.

There is a need to design incentive-compatible protocols, where participants are rewarded not just for joining but for contributing useful, high-quality updates [139]. This may involve cryptographic attestation, trust-weighted aggregation, or economic scoring systems. The challenge is balancing robustness (against manipulation) with inclusivity (avoiding exclusion of low-resource participants).

7.5. Benchmarking Environments and Open Federated Datasets

Progress in federated energy AI is hamstrung by the lack of standard evaluation environments. We need more than just datasets—we need federated learning environments that simulate realistic topologies, privacy constraints, failure modes, and regulatory heterogeneity [140,141].

Initiatives such as LEAF, FedML [142], and EnergyGym are steps in the right direction, but none currently offer energy-specific, reproducible FL scenarios with real economic constraints. Community effort is required to curate open benchmark suites that capture the diversity of use cases: forecasting, bidding, balancing, congestion control, etc.

7.6. Human-in-the-Loop and Operator Interpretability

One final gap—too rarely discussed—is human interpretability. Grid operators and market regulators will not deploy black-box federated models unless they understand their behavior and failure modes.

Research must address the explainability of federated models, especially in multi-agent setups. SHAP values, PDPs, or causal graphs must be adapted for federated contexts. Moreover, tools for monitoring and override—allowing human operators to pause, inspect, or revert federated updates—will be essential for trust and accountability (Figure 4, Table 3).

Future work should also explore the integration of FL with wide-area monitoring infrastructures. Leveraging PMUs and distributed PDCs as the data backbone for federated coordination could significantly enhance state estimation, fault detection, and stability control. This approach would combine the accuracy and responsiveness of synchronized phasor measurements with the privacy and scalability benefits of FL, creating a more robust foundation for real-time, decentralized grid optimization [143]. The decentralized grid optimization [143,144,145,146] can be practically realized through PMUs and PDCs that complete the hierarchical architecture discussed earlier in the paper. Optimal PMU placement under various uncertainties in distribution systems [143], topology observability, and maximum measurement redundancy in transmission systems [144], with synchronized measurements aggregated by PDCs in edge–cloud architectures [145], together constitute the backbone of a wide-area measurement system as described in [146].

Another critical direction is the application of federated learning in scenarios characterized by high renewable penetration and extreme operating conditions. As solar and wind generation introduce volatility and storage systems add complexity, models must adapt to rapid fluctuations and rare but impactful events such as storms, blackouts, or sudden frequency deviations. FL can play a vital role here by enabling distributed forecasting and control across heterogeneous assets without exposing raw operational data. By allowing local controllers to learn from extreme event patterns while contributing to a global model, federated approaches could improve resilience, support faster recovery, and enhance coordination in highly dynamic grids dominated by renewable energy sources.

Future work on federated learning in energy systems should integrate economic perspectives more explicitly. Issues such as market equilibrium, incentive compatibility, and strategic bidding are inherently game-theoretic, and FL offers an avenue for participants to coordinate without disclosing private information. Embedding such considerations into federated optimization could help ensure that technical solutions remain aligned with the realities of market design and policy.

Equally important is the development of open benchmark libraries that reflect realistic grid topologies, renewable penetration levels, and privacy policies. A shared benchmarking environment would enable reproducibility and comparability across studies, moving the field beyond proof-of-concept demonstrations. Such libraries could become a foundation for collaborative research, allowing utilities, regulators, and academic groups to evaluate federated algorithms under common scenarios and metrics.

8. Conclusions

The federated learning paradigm—so often romanticized as a privacy-preserving panacea—reveals itself, in the context of electricity markets, to be a far more ambiguous proposition. It is neither a plug-in solution nor a clear-cut innovation. Rather, it is a layered, sometimes contradictory, response to structural frictions: between local autonomy and system-wide coordination, between data richness and regulatory opacity, and between algorithmic convergence and physical feasibility.

This review has attempted not to resolve these tensions, but to map them—through architectures, use cases, methodological inconsistencies, and emergent research trajectories. We have seen that forecasting, bidding, congestion control, and maintenance can all benefit from FL under certain conditions. And yet, across all use cases, one constant remains: deployment realism lags behind architectural ambition.

If there is one lesson to carry forward, it is this: federated learning in energy systems is not a technological intervention—it is a political and infrastructural commitment. It presupposes a baseline of interoperability, institutional alignment, and incentive compatibility that, in many grid contexts, simply does not exist. The barriers are not solely in code or model structure, but in legacy systems, market design, and the asymmetries of who controls data and who benefits from learning.

And yet this is not a reason to abandon the project. Quite the opposite. It is a reason to proceed with greater humility—and greater interdisciplinarity. The next generation of FL systems in energy must be co-designed: not just by data scientists and control engineers but by regulatory scholars, human–computer interaction experts, and economists who understand the idiosyncrasies of energy justice and market behavior.

The path forward will not be clean. Federated models will diverge, sync imperfectly, fail silently. Incentives will misfire. Code will break. But somewhere in that imperfect loop—between local learning and global stability—there lies the architecture of a grid that is not only intelligent, but cooperative. A grid that learns—not because it must—but because it is designed to.

This review, then, is not a blueprint. It is a provocation. Beyond outlining architectures, use cases, and research priorities, this review emphasizes the importance of methodological coherence. The field cannot mature without common ground in evaluation: federated models must be assessed not only against local baselines but also in relation to centralized benchmarks, with attention to both predictive performance and computational overhead. By highlighting these gaps, we aim to encourage the development of standardized, domain-specific benchmarking practices that will allow future work to move beyond proof-of-concept studies toward robust, reproducible, and comparable results.