Byzantine-resilient Federated Learning Employing Normalized Gradients on
Non-IID Datasets
Byzantine-resilient Federated Learning Employing Normalized Gradients on
Non-IID Datasets
In practical federated learning (FL) systems, the presence of malicious Byzantine attacks and data heterogeneity often introduces biases into the learning process. However, existing Byzantine-robust methods typically only achieve a compromise between adaptability to different loss function types (including both strongly convex and non-convex) and robustness to heterogeneous datasets, but …