Dynamic Regret for Byzantine-Robust Online Federated Learning

Federated learning enables decentralized model training across multiple clients without exchanging raw data, making it a crucial paradigm for privacy-preserving machine learning. However, its deployment in adversarial and dynamic environments remains fundamentally challenging, particularly under Byzantine attacks. Existing online Byzantine-robust methods face two critical limitations: (i) a strong reliance on the Independent and Identically Distributed (IID) assumption to achieve sublinear regre