Robust Distributed Learning: Challenges and Solutions

This chapter explores the significance of robust distributed learning in training machine learning models across multiple devices while safeguarding privacy. It addresses vulnerabilities related to adversarial attacks, particularly Byzantine attacks, and discusses optimization strategies, including federated learning and robust averaging techniques. The chapter also highlights the importance of security measures and adaptive methods to enhance the reliability and efficiency of distributed training processes.