Federated Edge Intelligence for Next Generation AI Systems

Topic Information

Dear Colleagues,

In the context of 6G networks and advanced edge AI (artificial intelligence) applications, Federated Edge Intelligence (FEI) is rapidly becoming a key technology for realizing distributed, scalable, and privacy-preserving AI systems. FEI is a new type of distributed intelligence architecture that combines edge computing, federated learning (FL), and AI technologies and has remarkably advanced and huge application potential. Its main advantages lie in data privacy protection, low-latency response, and resource optimization. At the application level, FEI supports multi-modal data processing and can fuse visual, audio, tactile, radar, and other sensor data for real-time analysis and decision-making, representing significant application value in the fields of smart cities, autonomous driving, the industrial Internet, healthcare, financial economics, and other fields. Moreover, FEI’s edge computing architecture can be adaptively adjusted according to the computing power of different nodes, thus meeting real-time decision-making needs and providing more efficient computing and resource allocation. However, despite the many advantages of FEI, its realization still faces certain challenges. For example, edge nodes usually have more limited computing power and storage resources, and how to efficiently run complex AI models and conduct local training under these constraints remains an important issue. How to effectively synchronize models from different edge nodes and ensure the consistency of model aggregation is also an important challenge for FL in edge computing environments. 

We invite researchers to submit original, high-quality papers related to the above topics. All papers will undergo a rigorous peer review process. Submissions should clearly demonstrate innovative approaches, experimental results, and potential applications, particularly in advancing the field of Federated Edge Intelligence. 

This Special Issue covers the following broad topics related to FEI, specifically including, but not limited to, the following:

1. Federated learning at the edge:

• Model distribution and synchronization techniques in federated edge networks.
• Edge AI model optimization for real-time and end-device applications.
• Data upload/download mechanisms in federated learning.
• Model uploading and aggregation for local training. 

2. AI models in edge nodes:

• AI algorithms for multi-modal sensing in edge AI devices, including vision, audio, radar, touch, and smell.
• Edge node architectures and platform designs that support multiple AI models.
• Neuromorphic computing and pulsed neural networks (SNNs) in edge AI.

3. AI integration at the edge with the cloud:

• Task offloading techniques from edge devices to central cloud servers.
• Efficient communication and data-sharing protocols between edge nodes and cloud servers.
• Cloud model aggregation methods based on distributed edge node contributions.

4. Applications of Federated Edge Intelligence:

• Real-time applications such as autonomous driving, smart cities, the IoT, and healthcare.
• Support of intelligent edge computing for multi-sensor systems in 6G environments.
• Applications of Federated Edge Intelligence in industry.
• Federated learning in smart education.

Dr. Chunjiong Zhang
Dr. Weiwei Jiang
Dr. Tao Xie
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	19.2 Days	CHF 1800	Submit
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Computers computers	4.2	7.5	2012	17.5 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
IoT IoT	2.8	8.7	2020	25.5 Days	CHF 1400	Submit
Applied System Innovation asi	3.7	9.9	2018	22 Days	CHF 1600	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
AI Sensors aisens	-	-	2025	15.0 days *	CHF 1000	Submit

* Median value for all MDPI journals in the second half of 2025.

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

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2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (4 papers)

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23 pages, 1439 KB  

Open AccessArticle

A Layered Architecture for Concurrent CSI-Based Applications in Smart Environments

by Shervin Mehryar

IoT 2026, 7(1), 20; https://doi.org/10.3390/iot7010020 - 17 Feb 2026

Abstract

The prevalence of radio frequency signals in indoor environments has in recent years given rise to new technologies across many domains such as robotics, healthcare, and surveillance. Radio frequency signals propagate in the wireless medium through multiple paths and carry useful environment-dependent information. [...] Read more.

The prevalence of radio frequency signals in indoor environments has in recent years given rise to new technologies across many domains such as robotics, healthcare, and surveillance. Radio frequency signals propagate in the wireless medium through multiple paths and carry useful environment-dependent information. Capturing and analyzing these signal patterns can offer new solutions for a number of applications relevant to ranging, tracking, perception and recognition. In this work we propose a novel architecture, separating physical, back-bone networks, and inference layers, towards fully ubiquitous passive recognition systems that scale with the number of environments and applications. We propose a back-bone architecture that utilizes a novel Cross Dual-Path Attention (CDPA) block to capture spatial and temporal correlations from Channel State Information (CSI) for device-free, multi-task applications. Subsequently, a distill and transfer algorithm is proposed to generalize the inference capabilities of CDPA over multiple target environments for scalable training and reduced computational costs. By sharing knowledge between models across a shared network, experimentation shows that edge devices can be deployed with improved performance while simultaneously meeting strict computation and memory requirements. Our distributed learning paradigm demonstrates that CDPA-based models are capable of using passive signals in a non-intrusive and privacy-protecting manner, in order to achieve ubiquitous recognition at scale in smart environments. Full article

(This article belongs to the Topic Federated Edge Intelligence for Next Generation AI Systems)

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17 pages, 1153 KB  

Open AccessArticle

A Federated Deep Q-Network Approach for Distributed Cloud Testing: Methodology and Case Study

by Aicha Oualla, Oussama Maakoul, Salma Azzouzi and My El Hassan Charaf

AI 2026, 7(2), 46; https://doi.org/10.3390/ai7020046 - 1 Feb 2026

Viewed by 223

Abstract

The rapid expansion of the Internet of Things (IoT) has brought forth numerous challenges in testing distributed applications within cloud environments. A significant issue is the latency associated with hosting these applications on cloud computing platforms, despite their potential to improve productivity and [...] Read more.

The rapid expansion of the Internet of Things (IoT) has brought forth numerous challenges in testing distributed applications within cloud environments. A significant issue is the latency associated with hosting these applications on cloud computing platforms, despite their potential to improve productivity and reduce costs. This necessitates a reevaluation of existing conformance testing frameworks for cloud environments, with a focus on addressing coordination and observability challenges during data processing. To tackle these challenges, this study proposes a novel approach based on Deep Q-Networks (DQN) and federated learning (FL). In this model, fog nodes train their local models independently and transmit only parameter updates to a central server, where these updates are aggregated into a global model. The DQN agents replace explicit coordination messages with learned decision functions, dynamically determining when and how testers should coordinate. This approach not only preserves the privacy of IoT devices but also enhances the efficiency of the testing process. We provide a comprehensive mathematical formulation of our approach, along with a detailed case study of a Smart City Traffic Management System. Our experimental results demonstrate significant improvements over traditional testing approaches, including a ~58% reduction in coordination messages. These findings confirm the effectiveness of our approach for distributed testing in dynamic environments with varying network conditions. Full article

(This article belongs to the Topic Federated Edge Intelligence for Next Generation AI Systems)

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29 pages, 13037 KB  

Open AccessArticle

Energy-Efficient Hierarchical Federated Learning in UAV Networks with Partial AI Model Upload Under Non-Convex Loss

by Hui Li, Shiyu Wang, Yu Du, Runlei Li, Xin Fan and Chuanwen Luo

Sensors 2026, 26(2), 619; https://doi.org/10.3390/s26020619 - 16 Jan 2026

Viewed by 212

Abstract

Hierarchical Federated Learning (HFL) alleviates the trade-off between communication overhead and privacy protection in mobile scenarios via multi-level aggregation and mobility consideration. However, its idealized convex loss assumption and full-dimension parameter upload deviate from real-world non-convex tasks and edge channel constraints, causing excessive [...] Read more.

Hierarchical Federated Learning (HFL) alleviates the trade-off between communication overhead and privacy protection in mobile scenarios via multi-level aggregation and mobility consideration. However, its idealized convex loss assumption and full-dimension parameter upload deviate from real-world non-convex tasks and edge channel constraints, causing excessive energy consumption, high communication cost, and compromised convergence that hinder practical deployment. To address these issues in mobile/UAV networks, this paper proposes an energy-efficient optimization scheme for HFL under non-convex loss, integrating a dynamically adjustable partial-dimension model upload mechanism. By screening key update dimensions, the scheme reduces uploaded data volume. We construct a total energy minimization model that incorporates communication/computation energy formulas related to upload dimensions and introduces an attendance rate constraint to guarantee learning performance. Using Lyapunov optimization, the long-term optimization problem is transformed into single-round solvable subproblems, with a step-by-step strategy balancing minimal energy consumption and model accuracy. Simulation results show that compared with the original HFL algorithm, our proposed scheme achieves significant energy reduction while maintaining high test accuracy, verifying the positive impact of mobility on system performance. Full article

(This article belongs to the Topic Federated Edge Intelligence for Next Generation AI Systems)

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19 pages, 2248 KB  

Open AccessArticle

A Platform for Machine Learning Operations for Network Constrained Far-Edge Devices

by Calum McCormack and Imene Mitiche

Appl. Syst. Innov. 2025, 8(5), 141; https://doi.org/10.3390/asi8050141 - 28 Sep 2025

Viewed by 1550

Abstract

Machine Learning (ML) models developed for the Edge have seen a massive uptake in recent years, with many types of predictive analytics, condition monitoring and pre-emptive fault detection developed and in-use on Internet of Things (IoT) systems serving industrial power generators, environmental monitoring [...] Read more.

Machine Learning (ML) models developed for the Edge have seen a massive uptake in recent years, with many types of predictive analytics, condition monitoring and pre-emptive fault detection developed and in-use on Internet of Things (IoT) systems serving industrial power generators, environmental monitoring systems and more. At scale, these systems can be difficult to manage and keep upgraded, especially those devices that are deployed in far-Edge networks with unreliable networking. This paper presents a simple and novel platform architecture for deployment and management of ML at the Edge for increasing model and device reliability by reducing downtime and access to new model versions via the ability to manage models from both Cloud and Edge. This platform provides an Edge ML Operations “Mirror” that replicates and minimises cloud MLOps systems to provide reliable delivery and retraining of models at the network Edge, solving many problems associated with both Cloud-first and Edge networks. The paper explores and explains the architecture and components of the system, offering a prototype system that was evaluated by measuring time to deploy models with regard to differing network instabilities in a simulated environment to highlight the necessity for local management and federated training of models as a secondary function to Cloud model management. This architecture could be utilised by researchers to improve the deployment, recording and management of ML experiments on the Edge. Full article

(This article belongs to the Topic Federated Edge Intelligence for Next Generation AI Systems)

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