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

arXiv: https://arxiv.org/abs/2509.19398 · HTML: https://mlsanity.com/paper/2509.19398/

## 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.

## Builds on (most cited first, 22 of 22)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [Device Scheduling with Fast Convergence for Wireless Federated Learning](https://mlsanity.com/paper/1911.00856/) (2019)
- [Energy-Efficient Radio Resource Allocation for Federated Edge Learning](https://mlsanity.com/paper/1907.06040/) (2019)
- [FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data](https://mlsanity.com/paper/2403.16460/) (2024)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices](https://mlsanity.com/paper/2402.15903/) (2024)
- [Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities](https://mlsanity.com/paper/2304.11397/) (2023)
- [Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks](https://mlsanity.com/paper/2301.06646/) (2023)
- [On the Convergence of Multi-Server Federated Learning with Overlapping Area](https://mlsanity.com/paper/2208.07893/) (2022)
- [FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems](https://mlsanity.com/paper/2107.02755/) (2021)
- [Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems](https://mlsanity.com/paper/1903.08566/) (2019)
- [Sense4FL: Vehicular Crowdsensing Enhanced Federated Learning for Object Detection in Autonomous Driving](https://mlsanity.com/paper/2503.17697/) (2025)

## 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.