Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

TL;DR

This paper proposes a novel resource allocation framework for multiple federated learning services sharing wireless network resources, optimizing bandwidth distribution to improve efficiency, fairness, and privacy.

Contribution

It introduces a two-level resource allocation framework and designs algorithms for both cooperative and selfish FL service providers, addressing a gap in wireless FL resource management.

Findings

Distributed bandwidth allocation improves FL round efficiency.

Auction scheme balances FL performance and fairness.

Algorithms outperform benchmarks in simulations.

Abstract

This paper studies a federated learning (FL) system, where \textit{multiple} FL services co-exist in a wireless network and share common wireless resources. It fills the void of wireless resource allocation for multiple simultaneous FL services in the existing literature. Our method designs a two-level resource allocation framework comprising \emph{intra-service} resource allocation and \emph{inter-service} resource allocation. The intra-service resource allocation problem aims to minimize the length of FL rounds by optimizing the bandwidth allocation among the clients of each FL service. Based on this, an inter-service resource allocation problem is further considered, which distributes bandwidth resources among multiple simultaneous FL services. We consider both cooperative and selfish providers of the FL services. For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients. For selfish FL service providers, a new auction scheme is designed with the FL service owners as the bidders and the network provider as the auctioneer. The designed auction scheme strikes a balance between the overall FL performance and fairness. Our simulation results show that the proposed algorithms outperform other benchmarks under various network conditions.