
Asynchronous Byzantine Federated Learning
We propose an asynchronous, Byzantine-resilient FL algorithm that avoids straggler delays and requires no server dataset. By updating after a safe number of client contributions, it outperforms state-of-the-art methods, achieving faster training and higher accuracy under multiple attack types.
