Decentralized Learning Algorithms: A Systems View

This lecture explores the challenges of Federated Learning (FL) and Decentralized Learning (DL) systems, focusing on issues like expensive communication and data center problems. The instructor discusses strategies to enhance these systems through decentralization, data heterogeneity, and privacy guarantees, presenting a TEE-based decentralized recommender system as a case study. The lecture also introduces the Rex++ extension for ML tasks and the GeL algorithm for boosting federated learning. Overall, the lecture provides insights into improving the scalability, privacy, and efficiency of decentralized learning algorithms.