On the Convergence of Multi-Server Federated Learning with Overlapping Area

TL;DR

This paper introduces a novel multi-server federated learning algorithm with overlapping coverage areas, providing convergence analysis for non-iid data and strategies to accelerate training in complex network topologies.

Contribution

The paper proposes the MS-FedAvg algorithm for multi-server FL with overlapping areas and analyzes its convergence under various participation schemes.

Findings

Full client participation is feasible due to smaller client groups per server.

Convergence depends on the ratio of clients in each area to total clients.

Biased partial participation accelerates convergence.

Abstract

Multi-server Federated learning (FL) has been considered as a promising solution to address the limited communication resource problem of single-server FL. We consider a typical multi-server FL architecture, where the coverage areas of regional servers may overlap. The key point of this architecture is that the clients located in the overlapping areas update their local models based on the average model of all accessible regional models, which enables indirect model sharing among different regional servers. Due to the complicated network topology, the convergence analysis is much more challenging than single-server FL. In this paper, we firstly propose a novel MS-FedAvg algorithm for this multi-server FL architecture and analyze its convergence on non-iid datasets for general non-convex settings. Since the number of clients located in each regional server is much less than in single-server FL, the bandwidth of each client should be large enough to successfully communicate training models with the server, which indicates that full client participation can work in multi-server FL. Also, we provide the convergence analysis of the partial client participation scheme and develop a new biased partial participation strategy to further accelerate convergence. Our results indicate that the convergence results highly depend on the ratio of the number of clients in each area type to the total number of clients in all three strategies. The extensive experiments show remarkable performance and support our theoretical results.