FedOC: Multiserver FL With Overlapping Client Relays in Wireless Edge Networks

Multi-server federated learning (FL) has emerged as a promising paradigm to alleviate the communication bottlenecks of single-server FL by exploiting edge-level aggregation. In realistic dense deployments, the coverage regions of neighboring edge servers (ESs) often overlap, enabling some clients to communicate with multiple ESs. However, existing multi-server FL schemes fail to fully leverage such overlapping clients for efficient inter-server collaboration, leading to excessive reliance on cloud aggregation and slow convergence under heterogeneous data distributions. To address this challenge, we propose FedOC, a novel multi-server FL framework that explicitly exploits overlapping clients to accelerate training. In FedOC, overlapping clients can serve as relay overlapping clients (ROCs) to enable real-time edge-to-edge model relaying, or as normal overlapping clients (NOCs) that dynamically select edge models for local training based on delivery latency, thereby facilitating indirect data fusion across ESs. By integrating decentralized inter-ES aggregation with latency-aware client training, FedOC significantly reduces the need for frequent cloud aggregation while improving training efficiency. Extensive experiments on MNIST, Fashion-MNIST and CIFAR-10 demonstrate that FedOC achieves substantially faster convergence and higher final accuracy than other baselines. In particular, under strong data heterogeneity and cloud-free aggregation, FedOC improves the final test accuracy by approximately 3%–25% on these datasets compared with other baselines, while significantly reducing the latency required to reach a target accuracy.