Oort - Efficient Federated Learning via Guided Participant Selection

Explore an innovative approach to federated learning in this conference talk from OSDI '21. Delve into Oort, a guided participant selection system designed to enhance the efficiency of federated training and testing. Learn how Oort prioritizes clients with high-utility data and fast training capabilities to improve time-to-accuracy performance and final model accuracy. Discover techniques for interpreting results in model testing while meeting specific distribution requirements. Examine the challenges of identifying heterogeneous client utility, selecting high-utility clients at scale, and adapting selection processes. Gain insights into the anatomy of time-to-accuracy in training and the statistical performance improvements achieved through this novel approach to federated learning.

Intro
Emerging Trend of Machine Learning
Emerging Federated Learning on the Edge
Execution of Federated Learning (FL)
Challenges in Federated Learning
Existing Client Selection: Suboptimal Efficiency
Existing Client Selection: Unable for Selection Criteria
Oort: Guided Participant Selection for FL
Anatomy of Time to Accuracy in Training
Challenge I: Identify Heterogeneous Client Utility
Challenge 2: Select High-Utility Clients at Scale
Challenge 3: Select High-Utility Clients Adaptively
Time-to-Accuracy (TTA) Performance
Zoom into Statistical Performance