City Research Online

Abstract

Federated learning (FL) enables collaborative model training without centralising raw data, but its performance is susceptible to label noise from clients. A common mitigation strategy involves using a clean, labelled public dataset at the server to assess client reliability. However, this approach is impractical due to the unrealistic assumption of availability of a clean, labelled public dataset. To address this issue, we propose FedDS, a novel approach that brings the Dawid-Skene model from statistical analysis to FL, which enables the estimation of the reliability of each client in FL without requiring any labelled data at the server. This approach effectively mitigates the adverse impact of heterogeneous label noise under a weaker and more practical assumption, offering a robust aggregation strategy for real-world FL scenarios with label noise. The code is available at https://github.com/Gia99999/FedDS.

Publication Type:	Article
Additional Information:	© 2026 The Authors. Published by Elsevier Inc. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Federated learning, Noisy labels, Dawid-Skene model, Client weighting
Subjects:	Q Science > QA Mathematics
Departments:	Bayes Business School Bayes Business School > Faculty of Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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