Similarity-based Filtering for Defending Against Malicious Clients in Federated Learning

Federated Learning (FL) enables the training of machine learning models across decentralized clients holding local data samples without exchanging them. While this paradigm preserves data privacy, it is vulnerable to adversarial attacks from malicious clients that can poison the global model by submitting harmful updates. Label-flipping attacks, where attackers intentionally mislabel their training data, pose a significant threat due to their subtlety and similarity to legitimate updates. In this work, we introduce a lightweight and effective filtering system that leverages distance and similarity metrics between client updates and a trimmed mean to identify and exclude malicious updates. Through experiments on the CIFAR-10 dataset with non-independent and identically distributed (Non-IID) data distributions, we demonstrate that our approach improves the robustness of the global model against label-flipping attacks, maintaining accuracy levels close to those achieved without any attackers.