“Adaptation and Federated Learning”
Speaker: Mehryar Mohri, Professor of Computer Science and Mathematics, NYU
Federated learning is a prominent new learning scenario in large-scale applications, where a centralized model is trained based on data originating from a large number of clients. But, how can learning be successful with heterogenous client distributions? This talk discusses fundamental theoretical and algorithmic solutions for several variants of this problem.
This event was a part of the YINS Weekly Seminar Program and was presented on Wednesday, March 10, 2021.
