Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning

Understand

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 model across multiple rounds.
• At each round, the same server model is broadcast to all participating clients, updated locally, and then aggregated across clients.
• In this work, we propose a more general procedure in which clients "select" what values are sent to them.