BPFLH: Byzantine-Robust Privacy-Preserving Federated Learning for Heterogeneous Data

Byzantine-robust federated learning (FL) aims to obtain an accurate global model even with potentially Byzantine users. However, most existing schemes rely on measuring the overall differences between the entire gradient vectors of different users, which fail to effectively distinguish malicious gradients from benign ones caused by data heterogeneity under non-IID settings, thereby compromising model performance. To tackle this challenge, we propose BPFLH, a novel Byzantine-robust privacy preserving FL framework for heterogeneous data. BPFLH is the first to introduce Bray–Curtis dissimilarity into FL, capturing the element-wise differences among gradients from different users. This method reduces the risk of misclassifying benign gradi ents as malicious and enhance the model's robustness against Byzantine attacks in non-IID data environments. Furthermore, BPFLH leverages CKKS homomorphic encryption to protect local gradients, enabling secure aggregation and Byzantine user detection without compromising privacy. Extensive experiments on real-world datasets under various attack scenarios and data distributions demonstrate that BPFLH exhibits strong robustness against Byzantine attacks while preserving privacy and maintaining superior accuracy compared to existing Byzantine-robust FL methods, particularly in non-IID environments.