Online Optimization Meets Federated Learning - Tutorial
Offered By: Uncertainty in Artificial Intelligence via YouTube
Course Description
Overview
Explore the intersection of online optimization and federated learning in this comprehensive 2-hour and 22-minute tutorial from the Uncertainty in Artificial Intelligence conference. Delve into state-of-the-art theoretical results in online and bandit convex optimization, federated/distributed optimization, and emerging findings at their convergence. Begin with an in-depth look at the Online Optimization setting, focusing on the adversarial model, regret notion, and various feedback models. Analyze performance guarantees of online gradient descent-based algorithms. Next, examine the Distributed/Federated Stochastic Optimization model, discussing data heterogeneity assumptions, local update algorithms, and min-max optimal algorithms. Highlight the scarcity of results beyond the stochastic setting, particularly in adaptive adversaries. Conclude by exploring the emerging field of Distributed Online Optimization, introducing a distributed notion of regret and recent developments in first and zeroth-order feedback. Gain insights into numerous open questions and practical applications of this framework, presented by experts Aadirupa Saha and Kumar Kshitij Patel.
Syllabus
UAI 2023 Tutorial: Online Optimization Meets Federated Learning
Taught by
Uncertainty in Artificial Intelligence
Related Courses
Reinforcement LearningIndian Institute of Technology Madras via Swayam Bandit Algorithm (Online Machine Learning)
Indian Institute of Technology Bombay via Swayam Reinforcement Learning
Edureka Bandits - Kevin Jamieson - University of Washington
Paul G. Allen School via YouTube Tracking Significant Changes in Bandit - IFDS 2022
Paul G. Allen School via YouTube