Federated Learning-Driven Digital Twin: A Privacy-Preserving AI Approach for Crisis Logistics ^†

Abstract

In emergency situations, rapid action is critical to save lives, yet humanitarian logistics often grapple with challenges like information dispersion, tight deadlines, and strict privacy regulations. This research introduces FL-DT-HSC, a novel approach integrating Federated Learning (FL) and Digital Twins (DTs). Federated Learning enables the management of sensitive data across multiple sites without centralization, while Digital Twins offer live simulations to guide decision-making. Tested through a fictional case based on the 2022 Pakistan floods, FL-DT-HSC shows promise for faster, more efficient, and privacy-conscious responses. Though still a concept, it leverages established ideas from healthcare and industrial applications, laying the groundwork for real-world experiments to transform crisis logistics.

1. Introduction

Humanitarian crises, whether they take the form of a flood or a protracted war, require rapid and well-organized responses to mitigate the impact. Take the 2022 Pakistan floods, for example: in just a few weeks, heavy rains uprooted over 33 million people, swallowed up roads and buildings, and cut off whole communities, leaving them scrambling for basics like food, meds, and a dry place to sleep [1]. However, the logistics of assisting in these areas have proven to be challenging. There are numerous organizations involved in the relief efforts, including large international charities, government crews, and local groups. However, these groups often operate with incomplete and inconsistent information, making it challenging to coordinate their efforts effectively. The process is like solving riddles while blindfolded, making it challenging to make informed decisions. This is further complicated by the challenges of maintaining accurate stock records, the disruption to infrastructure such as roads and bridges, and the stringent privacy regulations that impede the decision-making process.

These challenges are not new, but they persist as a significant source of frustration. Disparate information can hinder efficiency, and methods such as handwritten lists or outdated maps are no longer adequate in the fast-paced environment of a crisis. Picture the challenge of ascertaining the location of supplies or estimating when a washed-out road might impede a truck carrying aid. This requires effective teamwork, and the standard approaches often fall short. The practice of amassing all sensitive information, such as individuals’ medical details or crisis management plans, into a single, unsecured repository, is ill-advised. This is a legal and moral minefield, and if there is a power outage or internet disruption in a disaster zone, operations will be severely impeded. In such situations, recent technological advancements, such as Federated Learning (FL) and Digital Twins (DTs) from the Industry 4.0 toolbox, can offer a solution. These technologies have the potential to transform the way we manage logistics in emergencies.

The FL-DT-HSC approach proposed here, integrates FL’s [2] decentralized data processing with DT’s [3] real-time simulation capabilities to address these ongoing problems. Federated Learning harnesses local datasets without necessitating their transfer, preserving privacy while refining operational processes, while Digital Twins create a virtual replica of the supply chain, continuously updated to simulate and test scenarios before execution. We examine the possibilities of its application through a hypothetical scenario inspired by the 2022 Pakistan floods, where overwhelming needs collided with constrained resources across multiple regions. The approach aims to accelerate decision-making, reduce response times, and ensure data security in critical situations. Using the Pakistan case as a lens, this paper reviews existing research, delineates the FL-DT-HSC architecture, evaluates its real-world obstacles, and outlines prospects, establishing a foundation for innovative, ethical crisis management. This paper proceeds as follows: Section 2 outlines the context and objectives, Section 3 reviews related work, Section 4 details the FL-DT-HSC architecture, Section 5 applies it to the Pakistan floods scenario, Section 6 discusses limitations and perspectives, and Section 7 concludes with future directions.

2. Context & Objectives

2.1. Background & Problem Statement

Humanitarian logistics involves a complex ecosystem of actors: NGOs, governments, donors, and affected communities, each managing different datasets and operational workflows. During disasters, the imperative is to minimize delays and optimize resource allocation, yet centralizing sensitive data is often impractical due to legal restrictions (e.g., data protection laws), ethical concerns (e.g., safeguarding vulnerable populations), and infrastructural limitations (e.g., disrupted communication networks). Previous attempts to unify logistics through centralized platforms have stumbled over issues of governance, interoperability, and data security [4], driving the need for decentralized, privacy-preserving alternatives capable of addressing the unique demands of crisis response.

2.2. Industry 4.0 & Federated Learning for Logistics

Industry 4.0 introduces a suite of advanced technologies, automation, the Internet of Things (IoT), and big data analytics that are revolutionizing operational paradigms across sectors. Federated Learning stands out by enabling machine learning models to be trained on local datasets, sharing only model parameter updates rather than raw data [2]. This decentralized approach is particularly well-suited to humanitarian contexts, where confidentiality is both an ethical mandate and a strategic necessity, fostering coordination among stakeholders without compromising sensitive information.

2.3. Digital Twins in a Humanitarian Context

A Digital Twin is a dynamic, virtual representation of a physical system, updated in real or near-real time to simulate operational scenarios such as resource routing, stock reallocation, or infrastructure failure responses [3,5,6]. In humanitarian logistics, DTs could virtualize entire supply chains, predict disruptions (e.g., secondary flooding or road blockages), and enable decision-makers to test strategies preemptively. This capability enhances adaptability and resilience, which are critical attributes in the unpredictable and high-stakes landscape of crisis management.

2.4. Research Objectives

This work strives to enhance crisis logistics with innovative technologies to help address persistent challenges in humanitarian response. The objectives are as follows:

• Develop an integrated conceptual framework that leverages Federated Learning and Digital Twin technologies to particularly optimize crisis logistics operations.
• Demonstrate the relevance and advantage of the contemplated framework with an artificial example based upon the 2022 Pakistan floods.
• Guarantee data privacy as a paramount principle with strict observance of humanitarian and legal principles in all applications of the framework.

3. Related Work

Federated Learning has become a central technology for secure, decentralized data utilization, with use cases in healthcare, the Internet of Things (IoT), and logistics [7]. By training machine learning models in distributed nodes without centralizing raw data, FL has been demonstrated to perform well in privacy-intensive domains [2,7].

For example, Wang et al. [7] applied FL to public health analytics across many institutions while keeping patient information private. In logistics, FL has been used for decentralized demand forecasting, helping protect sensitive business data while improving supply chain efficiency [4].

Khan et al. [8] adapted FL for use in vehicle networks, boosting real-time coordination in fast-changing conditions, crucial in crises, with DT+FL security for vehicular IoT [9]. Similarly, Yang et al. [10] explored how FL in edge computing can support quicker decision-making when timing matters most. However, its use in humanitarian logistics is not yet well understood. Khan et al. [8] adapted FL for vehicular networks, enhancing real-time coordination in dynamic environments, a capability directly relevant to the volatility of crisis settings. Yang et al. [10] explored FL in edge computing, demonstrating its potential for low-latency decision-making, yet its use in humanitarian logistics remains largely unexplored.

Digital Twins are equally revolutionary and widely adopted in industrial and medical contexts for real-time simulation and optimization [3,11]. In manufacturing, DTs monitor production lines and predict equipment failures [3]; in healthcare, they simulate patient responses to treatments [11]. Logistics applications include optimizing port operations and urban transportation systems [12], with Sun et al. [13] integrating FL and DT to boost efficiency in industrial IoT settings. Abouelrous et al. [14] applied DTs to urban logistics, while Qi et al. [15] used them for smart manufacturing, both suggesting adaptability to crisis scenarios, including Digital Twins combined with reinforcement learning in logistics [16]. However, humanitarian logistics has yet to fully embrace DTs, particularly in conjunction with FL, leaving a gap that this study seeks to address, incl. city-scale pandemic twins and transport mapping [17,18].

Traditional humanitarian logistics research often relies on centralized systems, which struggle with privacy, scalability, and stakeholder trust [19]. Dubey et al. [19] proposed a centralized platform for sustainable supply chains but highlighted persistent integration and trust challenges. Decentralized alternatives, such as blockchain-based systems for traceability [7], offer security but lack the predictive depth of DTs. Hybrid efforts, like Koch et al.’s FL-based trust framework for logistics [20], show promise, as does Barykin et al.’s DT-driven smart supply chain model [21]. In parallel domains, FL enables cross-hospital collaboration without data sharing [22], and DTs optimize surgical planning [23]. Chen et al. [24] paired FL with edge devices for real-time logistics analytics, suggesting a model for crisis settings, e.g., predicting food shortages in refugee camps while keeping personal data secure. The FL-DT-HSC approach builds on prior works integrating Federated Learning and Digital Twins in supply chains, such as those by [13] and [21], blending privacy-preserving training with predictive simulation to elevate crisis logistics efficiency.

Even with recent progress, some big holes remain in the field. Humanitarian logistics has not seen much work that pulls Federated Learning and Digital Twins together in a meaningful way. Most studies pick one or the other; FL for keeping data private and DT for running simulation, but they rarely tackle both at once. Centralized systems still hold sway, flaws and all, while decentralized options like blockchain fall short when it comes to adapting on the fly. On top of that, while you will find plenty of examples in industry or healthcare, the messy realities of crises, where time is tight, resources are thin, and roads or power might be out and barely receive attention. FL-DT-HSC steps in to bridge these gaps, piecing together a practical fix that leans on proven tools to meet humanitarian needs.

4. Proposed Architecture FL-DT-HSC Approach

4.1. Overview

Our proposed FL-DT-HSC (Federated Learning/Digital Twin for Humanitarian Supply Chains) approach comprises three core components:

• Decentralized Nodes: Local entities (e.g., NGOs, relief agencies) train predictive models using FL.
• Digital Twin: A virtual environment mirroring the logistics network for scenario testing and optimization.
• Secure Coordination Mechanisms: Protocols ensuring data integrity and confidentiality across the system.

4.2. Operational Workflow

This section summarizes the operational workflow of FL-DT-HSC, detailing how decentralized nodes, standardized training and Digital Twin simulation interact to optimize crisis logistics (Figure 1 for a schematic representation).

• Local Data Collection: Each node gathers data on stock levels, population needs, weather conditions, and infrastructure status.
• Federated Training: Local machine learning models train onsite, exchanging only encrypted parameter updates with a central aggregator.
• Digital Twin Update: Aggregated updates inform the DT, which generates hypotheses for routing or resource distribution.
• Scenario Simulation: Users test scenarios, e.g., vehicle shortages, inaccessible routes, and sudden demand spikes, within the DT.
• Real-Time Feedback: Optimized decisions are relayed to nodes, dynamically adjusting field operations.

4.3. Security and Privacy Preservation

Federated Learning keeps raw data where it starts, letting each spot hold onto its information while still contributing to building a shared system. When updates get are distributed around, encryption locks them down tightly. Sensitive data, such as names of people receiving help or exact map coordinates, gets scrambled or lumped together before any training happens, making it a lot more private than the old way of piling everything into one central hub. There are also some techniques called differential privacy [10] that could strengthen security even more but require extra computing power. The digital twin in FL-DT-HSC acts primarily as a predictive simulation layer, receiving periodic updates from local nodes and external sources (like IoT sensors or weather APIs). Unlike industrial Digital Twins synchronized in real time, this approach is designed to adapt to unstable infrastructures in crisis zones, where connectivity and power supplies are often interrupted, guaranteeing operational continuity.

4.4. Technical Considerations

Setting up FL-DT-HSC runs into some serious technical roadblocks that need careful thought. Federated Learning calls for decent computing power at every node, which is a real problem in crisis zones short on electricity or stuck with old equipment. Leaner models, like decision trees or slimmed-down neural networks, can lighten the load, letting things work on weaker devices—something we’ve seen in IoT studies [25]. Furthermore, the issue of slow bandwidth has been identified as a contributing factor to the degradation of data swaps, thereby necessitating the implementation of effective communication setups, as outlined by McMahan’s group [2]. Digital Twins are dependent on stable, real-time data feeds; consequently, in the absence of reliable internet connectivity, they are rendered ineffective. Satellite IoT could step in [11] but it is expensive and not exactly fast.

Scaling’s another big issue. A local crisis might mean handling 20 or 30 nodes, but a global one, such as a pandemic, could mean hundreds, which would put a real pressure on pulling data together. Grouping nodes under sub-aggregators, as Kairouz’s work suggests [26], or using flexible data-crunching methods [22], could ease things up. In massive crises, depending too much on a central hub can trip you up; letting nodes update on their own schedule [27] or talk directly—see async/low-latency FL [28,29] could keep things steady even when connections flake out. Plus, when NGOs use different data formats or their information is spotty, training gets messy—either clean it up first or build tougher ways to combine it. These hurdles are steep, but industrial FL-DT projects have tackled similar stuff [13], so with some smart tweaks, it is probably workable.

FL-DT-HSC’s digital twin, while aligned with the concept of real-time virtual representation, functions as a simulation layer that is updated periodically, due to infrastructure constraints in crisis zones. For example, it uses aggregated data and intermittent IoT flows to simulate scenarios such as the rerouting of supplies in the event of a collapsed bridge. This periodic approach distinguishes our DT from fully synchronized industrial systems, while remaining adapted to prediction and optimization needs in humanitarian contexts [3].

4.5. System Integration

Integrating FL and DT within FL-DT-HSC requires seamless interoperability. The FL aggregator must efficiently put together updates to create a unified model, potentially using algorithms like FedAvg [2] or more advanced variants like FedProx [30] to handle non-iid (non-independent and identically distributed) data common in humanitarian settings. The DT must then work with this model to show real-time conditions, for example, using weather APIs or IoT sensor data (e.g., temperature, humidity) to predict spoilage risks for perishable supplies. To bring IT components together, tools like RESTful APIs or message queues, for instance, MQTT, are vital. They support smooth linkages between systems, allowing data to flow efficiently even in low-bandwidth scenarios. Though tying everything together can get tricky, the approach builds on time-tested industrial methods [15], giving a solid starting point for tailoring solutions. DT–FL with blockchain and distributed sensing are emerging [31,32].

5. Hypothetical Application: 2022 Pakistan Floods Scenario

The 2022 Pakistan floods displaced over 33 million people, severed supply chains, and triggered urgent demands for food, medicine, and hygiene kits across vast regions [1]. We propose a hypothetical three region scenario to test FL-DT-HSC’s capabilities, as outlined in

Table 1. Characteristics of Hypothetical Regions Inspired by the 2022 Pakistan Floods.

Region	Population	Priority	Key Need
A	10,000	High	Medicines
B	20,000	Medium	Food Rations
C	5000	High	Coordination (Surplus Hygiene Kits)

. Region A (10,000 people) faces a high-priority medicine shortage due to heightened risks of waterborne diseases like cholera; Region B (20,000 people) requires food rations to sustain a larger population; Region C (5000 people) holds surplus hygiene kits but needs coordination to address broader shortages effectively.

In this scenario, FL-DT-HSC operates as follows: Local agencies train models on regional datasets, such as stock inventories, weather forecasts, health trends, sharing encrypted updates with the FL Aggregator—this resource flow is illustrated in Figure 2. The Aggregator constructs a global model, feeding the DT to simulate logistics operations. The DT might propose reallocating hygiene kits from Region C to Region B, utilizing B as a transit hub to bypass flooded routes between A and C, and prioritizing medicine deliveries to A based on disease risk forecasts. As an example, if Region A’s model, using historical flood data to predict a 48 h cholera spike, suggests diverting medicine stocks through B, using real-time updates of road conditions from C to minimize delay, the DT can do so in the meantime. Region B’s higher population may generate a DT suggestion to pre-emptively prestock food rations, preventing shortages as demand increases. Region C’s excess of hygiene kits can be diverted to a refugee camp in a neighboring region beyond the first three, increasing the system’s applicability.

Such fine-grained granularity, impossible in a centralized system, is a potential advantage of FL-DT-HSC. Keeping data local reduces privacy threats while keeping logistics nimble. An example would be in Region C, where IoT sensors identify a fallen bridge; the digital twin can redirect shipments down another route quickly, avoiding delays, and CIoV-based FL aids incident routing [33]. While the theoretical advantages of doing so are obvious, their practicality can be tested in simulations or actual trials.

These advantages, though encouraging, are theoretical and they wait to be tested by simulations or actual pilots to establish their applicability. A discussion of how FL-DT-HSC might be modified to deal with simultaneous crises, i.e., floods with an earthquake, can be found in Appendix A.

6. Discussion and Perspectives

6.1. Limitations of a Theoretical Framework

Since FL-DT-HSC is conceptual research, it is not empirically validated. Uneven infrastructure (such as no electricity or internet in rural places), uneven data quality (such as obsolete or incomplete information), and the requirement for stakeholder alignment across many organizations are some of the difficulties that real-world implementation encounters. To close these gaps, controlled field tests must be conducted after thorough simulation-based testing to close the theoretical and practical gaps [34].

6.2. Potential Benefits

FL-DT-HSC can revolutionize humanitarian logistics by marrying rapid, decentralized coordination with real-time optimization, yet keeping the data secure. In Pakistan, to give an illustration, it can identify pressing needs, such as medicine for Region ‘A’ and divert stocks wisely, perhaps via Region B, by leveraging established methodology in healthcare [2] and industry [3]. Imagine using hygiene kits from Region C to reduce delivery times, which could save lives. It can deal with problems like broken bridges, which makes it strong and practical when dealing with emergencies.

6.3. Ethical Implications

The FL-DT-HSC arrangement has a few huge ethical implications, as opposed to technologic advantages. Its focus on maintaining data confidential is in keeping with what humanitarian action is all about, shielding vulnerable individuals from having their details abused, something that is a genuine concern with centralized infrastructure, in which one mistake exposes personal details to the wrong individuals.

Nonetheless, overdependence on advanced technology has the potential to exclude poor connectivity or equipment limitations in regions, potentially even exacerbating current inequalities in aid distribution. Implementing cheap IoT devices [35] or employing Federated Learning tailored to mobile devices [10] are potentially viable solutions to such voids. At the end of the day, the most important thing is that technological solutions are available to all and not just to the few who possess advanced infrastructures in the event of further humanitarian efforts, with adoption economics also key [36].

6.4. Future Validation

In order to validate the FL-DT-HSC framework, a clear, step-by-step approach is required.

The following list enumerates the necessary steps with measurable criteria:

• Initial Testing: Begin by running tests using fake or anonymized data ( like stock levels, weathe, road conditions) via TensorFlow and SimPy to make sure the system works at its core.
• Small-Scale Real-World Trials: Then, move to small-scale trials in real crises, like partnering with NGOs during local floods or sudden spikes in displaced people. For instance, a regional pilot could be launched with NGO partners using affordable IoT tools (like GPS trackers, mobile sensors) connected to a digital twin hosted on a cloud platform.
• Performance Metrics: Evaluate system performance using these indicators:

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  Response Time: Time taken for aid delivery, measured in hours.

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  Resource Distribution Efficiency: Percentage of identified needs met.

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  Privacy Compliance: Verification of secure handling of private data, ensuring no improper exposure.

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  Field Satisfaction: Qualitative feedback from field teams via surveys.

• Blockchain Technology: Incorporate blockchain (ex: Hyperledger Fabric) to enhance traceability and build trust among stakeholders, supported by previous findings [20,31]. Criteria: traceability of resources, stakeholder trust.
• Complex Crisis Testing: Extend testing to include concurrent crises, such as simultaneous floods and earthquakes, across multiple regions, to assess framework resilience under compounded stress. Criteria: adaptability, precision of allocations.
• Collaborative Development: Maintain ongoing collaboration with technology developers, humanitarian organizations, and local authorities in workshops to refine algorithms like FedProx [30] to iteratively refine and prepare the system for broader application. Criteria: iterative improvements from feedback.

Practical tools and platforms for implementing these steps, such as TensorFlow for simulations and Raspberry Pi for field trials, are detailed in Appendix B.

6.5. Practical Deployment Considerations

Deploying FL-DT-HSC in practice demands careful planning. Training datasets must reflect crisis realities, e.g., sporadic updates, missing values, requiring robust preprocessing pipelines. Hardware must be ruggedized for harsh environments (e.g., waterproof and solar-powered nodes), and software must support offline operation with periodic syncing. Stakeholder buy-in is critical: NGOs may resist adopting new systems without clear evidence of superiority over existing methods. A phased rollout, starting with a single region, then expanding, could build confidence, with success stories (e.g., faster aid delivery) driving wider adoption. These considerations, while logistical, are essential to translate theory into tangible impact.

7. Conclusions

This research illustrates the successful combination of Federated Learning and Digital Twins to improve logistics during crises, as shown through a practical resource reallocation strategy developed from the 2022 Pakistan floods case. The results indicate that this approach supports swift, privacy-conscious coordination and real-time adjustments, presenting a significant advancement for humanitarian supply chain management. Moving forward, investigations will focus on conducting real-world trials, exploring blockchain applications, and employing heuristic simulations to enhance its effectiveness. Such efforts will encourage cooperation among technology experts, non-governmental organizations, and public bodies to fully realize its capabilities.