Byzantine Resilience

Byzantine resilience in distributed machine learning focuses on developing algorithms that maintain accuracy and convergence even when a portion of participating nodes (agents or clients) behave maliciously or fail. Current research emphasizes robust aggregation techniques, often employing median-of-means or similar methods, and explores the impact of network topology and asynchronous communication on resilience, particularly within federated learning and decentralized settings. This field is crucial for securing the integrity and reliability of large-scale machine learning systems, enabling their deployment in sensitive applications where data privacy and robustness against adversarial attacks are paramount.