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FedOC: Multi-Server FL with Overlapping Client Relays in Wireless Edge Networks

arXiv 2509.19398 ↗First posted 23 Sep 2025cs.NI cs.AIcited by 0

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Abstract (arXiv)

Multi-server Federated Learning (FL) has emerged as a promising solution to mitigate communication bottlenecks of single-server FL. We focus on a typical multi-server FL architecture, where the regions covered by different edge servers (ESs) may overlap. A key observation of this architecture is that clients located in the overlapping areas can access edge models from multiple ESs. Building on this insight, we propose FedOC (Federated learning with Overlapping Clients), a novel framework designed to fully exploit the potential of these overlapping clients. In FedOC, overlapping clients could serve dual roles: (1) as Relay Overlapping Clients (ROCs), they forward edge models between neighboring ESs in real time to facilitate model sharing among different ESs; and (2) as Normal Overlapping Clients (NOCs), they dynamically select their initial model for local training based on the edge model delivery time, which enables indirect data fusion among different regions of ESs. The overall FedOC workflow proceeds as follows: in every round, each client trains local model based on the earliest received edge model and transmits to the respective ESs for model aggregation. Then each ES transmits the aggregated edge model to neighboring ESs through ROC relaying. Upon receiving the relayed models, each ES performs a second aggregation and subsequently broadcasts the updated model to covered clients. The existence of ROCs enables the model of each ES to be disseminated to the other ESs in a decentralized manner, which indirectly achieves intercell model and speeding up the training process, making it well-suited for latency-sensitive edge environments. Extensive experimental results show remarkable performance gains of our scheme compared to existing methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 49 references that are in the map. First posted on arXiv 23 Sep 2025.

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22 of 49 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2018Federated Learning with Non-IID Datacited by 1,108
  5. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  6. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  8. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  9. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  10. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  11. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  12. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
10 more
  1. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  2. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  3. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  4. 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devicescited by 16
  5. 2023Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Citiescited by 15
  6. 2023Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networkscited by 11
  7. 2022On the Convergence of Multi-Server Federated Learning with Overlapping Areacited by 8
  8. 2021FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systemscited by 6
  9. 2019Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systemscited by 4
  10. 2025Sense4FL: Vehicular Crowdsensing Enhanced Federated Learning for Object Detection in Autonomous Drivingcited by 4

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