Byzantine Robust

Byzantine robustness in distributed machine learning focuses on developing algorithms and systems resilient to malicious or faulty nodes ("Byzantine workers") that can disrupt the training process by sending corrupted data or model updates. Current research emphasizes developing efficient and communication-friendly aggregation methods, often incorporating techniques like trimmed mean, median, or more sophisticated approaches such as quadratic voting or dynamic defense strategies, to filter out or mitigate the impact of Byzantine nodes in various architectures, including centralized, decentralized, and federated learning settings. This field is crucial for ensuring the reliability and security of large-scale machine learning systems, particularly in applications where data privacy and model integrity are paramount.