Byzantine Robustness

Learning from History for Byzantine Robust Optimization

Karimireddy, He, and Jaggi (EPFL, arXiv:2012.10333, 2021) propose using historical gradient information to detect and filter Byzantine workers in distributed training, achieving near-optimal convergence even with a constant fraction of corrupt workers. The historical approach is notable because it breaks the fundamental limitation of single-round Byzantine filters without requiring cryptographic overhead.