Multi-Server FL with Overlapping Clients: A Latency-Aware Relay Framework

TL;DR

This paper introduces a latency-aware relay framework for multi-server federated learning with overlapping clients, optimizing model dissemination across edge servers without new communication links, and demonstrates significant performance improvements.

Contribution

It proposes a novel relay framework leveraging overlapping clients for multi-hop model exchange, with a theoretical convergence bound and an optimized routing strategy.

Findings

Achieves wider model dissemination coverage among edge servers.

Provides a new convergence upper bound for non-convex FL objectives.

Shows significant performance gains over existing methods.

Abstract

Multi-server Federated Learning (FL) has emerged as a promising solution to mitigate communication bottlenecks of single-server FL. In a typical multi-server FL architecture, the regions covered by different edge servers (ESs) may overlap. Under this architecture, clients located in the overlapping areas can access edge models from multiple ESs. Building on this observation, we propose a cloud-free multi-server FL framework that leverages Overlapping Clients (OCs) as relays for inter-server model exchange while uploading the local updated model to ESs. This enables ES models to be relayed across multiple hops through neighboring ESs by OCs without introducing new communication links. We derive a new convergence upper bound for non-convex objectives under non-IID data and an arbitrary number of cells, which explicitly quantifies the impact of inter-server propagation depth 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 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. This enables ES models to be relayed across multiple hops through neighboring ESs by OCs, achieving the widest possible transmission coverage for each model without introducing new communication links. Extensive experimental results show remarkable performance gains of our scheme compared to existing methods.