Federated Learning (FL) enables collaborative model training across multiple clients without sharing private data. We consider FL scenarios wherein FL clients are subject to adversarial (Byzantine) attacks, while the FL server is trusted (honest) and has a trustworthy side dataset. This may correspond to, e.g., cases where the server possesses trusted data prior to federation, or to the presence of a trusted client that temporarily assumes the server role. Our approach requires only two honest participants, i.e., the server and one client, to function effectively, without prior knowledge of the number of malicious clients. Theoretical analysis demonstrates bounded optimality gaps even under strong Byzantine attacks. Experimental results show that our algorithm significantly outperforms standard and robust FL baselines such as Mean, Trimmed Mean, Median, Krum, and Multi-Krum under various attack strategies including label flipping, sign flipping, and Gaussian noise addition across MNIST, FMNIST, and CIFAR-10 benchmarks using the Flower framework.
@article{arxiv.2508.12672,
title = {Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering},
author = {Emmanouil Kritharakis and Dusan Jakovetic and Antonios Makris and Konstantinos Tserpes},
journal= {arXiv preprint arXiv:2508.12672},
year = {2026}
}
Comments
Accepted at the 3rd Workshop on Advancements in Federated Learning (WAFL@ECML-PKDD 2025)