Decentralized Optimization

This lecture delves into the practical aspects of machine learning, focusing on methods applied to deep learning, optimization, and privacy concerns. The instructor discusses federated learning, where data remains on devices, and collaborative learning, emphasizing the importance of keeping data private. The lecture explores the concept of decentralized training, using techniques like federated averaging and secure aggregation to ensure robustness against malicious participants. Additionally, the instructor explains the trade-off between robustness and fairness in collaborative learning, highlighting the significance of using the median instead of the average to exclude outliers. The lecture also touches on adversarial robustness and system optimization for efficient training, concluding with insights on hyperparameter optimization and meta-learning.