198 citations · 764 across the 28 of their papers we have counts for
3 papers · 1 filter
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning
Zachary Charles, Kallista Bonawitz, Stanislav Chiknavaryan +2
Federated learning (FL) is a framework for machine learning across heterogeneous client devices in a privacy-preserving fashion. To date, most FL algorithms learn a "global" server…
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
Shanshan Wu, Tian Li, Zachary Charles +4
Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been purported to improve metr…
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
Nicole Mitchell, Johannes Ballé, Zachary Charles +1
A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server. We present a comprehensive…