Lecture
This lecture by the instructor covers the challenges and solutions for scalable and trustworthy learning in heterogeneous networks, focusing on data heterogeneity, privacy concerns, fairness, and robustness in federated learning. The presentation delves into practical deployments, federated optimization methods, impact on industry adoption, and the implications of heterogeneity on optimization and fairness. It also explores the concept of tilted empirical risk minimization and the importance of personalization for achieving fairness and robustness in federated learning.