FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning

Mingkun Yang, Ran Zhu, Qing Wang, Jie Yang

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Keywords: Federated Learning

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TL;DR: Client-transparent utility estimation for Federated Learning

Abstract: Federated Learning (FL) is an important privacy-preserving learning paradigm that plays an important role in the Intelligent Internet of Things. Training a global model in FL, however, is vulnerable to the noise in the heterogeneous data across the clients. In this paper, we introduce **FedTrans**, a novel client-transparent client utility estimation method designed to guide client selection for noisy scenarios, mitigating performance degradation problems. To estimate the client utility, we propose a Bayesian framework that models client utility and its relationships with the weight parameters and the performance of local models. We then introduce a variational inference algorithm to effectively infer client utility, given only a small amount of auxiliary data. Our evaluation demonstrates that leveraging FedTrans as a guide for client selection can lead to a better accuracy performance (up to 7.8\%), ensuring robustness in noisy scenarios.

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Primary Area: societal considerations including fairness, safety, privacy

Submission Number: 3443