FedSUV: Validity and Utility-guided Client Selection for Federated Learning

Abstract

Federated Learning faces significant challenges arising from two critical uncertainties: the validity of a client’s participation, which can be compromised by network and system heterogeneity, and the utility of the data contributed by each client, which varies due to heterogeneous statistical data. Traditional client selection methods often treat these uncertainties as a whole, leading to suboptimal performance. To address this issue, we propose FedSUV, an innovative client selection framework that decouples validity and utility uncertainties. FedSUV approaches client selection from a multi-objective optimization perspective, employing advanced bandit algorithms: a confidence bound-based linear contextual bandit for assessing validity and a Gaussian Process bandit for evaluating utility. We validate the effectiveness of FedSUV through both theoretical analysis and large-scale experiments conducted within our physical cluster.

Xiaosong Chen

2022 - Current

My research interest includes online algorithm and its application to cloud-edge systems.

Wenyan Chen

2021 - 2025 PhD student

My research interests include resource management and task scheduling in GPU clusters.

Yuanhang Chen

2023 - Current

My research interests include federated learning systems.

Huanle Xu

2021 - Current

I am currently an assistant professor from the Department of Computer and Information Scicence, Univeristy of Macau.