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.