Latency-Aware Federated Learning over Multiple Servers with Overlapping Service Areas

Multi-server Federated Learning (FL) has emerged as a promising approach to alleviate the communication bottle-necks of traditional single-server FL. In practical deployments, the coverage areas of different edge servers (ESs) may overlap, allowing clients in overlapping regions to access models from multiple ESs. Leveraging this observation, we enable overlapping clients (OCs) to relay edge models between neighboring ESs and dynamically select suitable models for local training, facilitating multi-hop model propagation across ESs without relying on frequent cloud aggregation. This design significantly reduces communication latency while improving training efficiency. We derive a convergence upper bound for the above OCs-based FL framework, which explicitly quantifies the impact of inter-server propagation on convergence error. Guided by this theoretical result, we formulate an optimization problem that aims to maximize dissemination range of each ES model among all ESs by OCs within a limited latency. To solve this problem, we develop a conflict-graph-based local search algorithm optimizing the routing strategy and scheduling the transmission times of individual ESs to its neighboring ESs. By integrating decentralized inter-ES aggregation with latency-aware client training, our proposed algorithm significantly reduces the need for frequent cloud aggregation while improving training efficiency. Extensive experimental results show remarkable performance gains of our scheme compared to existing state-of-the-art methods. © 2026 IEEE.