Sketched and clustered federated learning with automatic tuning

Abstract

A computer-implemented method, a computer program product, and a computer system for automatic adaptive client selection in federated learning. A server sends parameters of a machine learning model to all of clients, where all of the clients compute respective gradients using the parameters. The server receives sketches of the respective gradients, where the sketches are computed by all of the clients. The server uses the sketches to compute similarity between all of the clients and clusters the all of the clients based on the similarity. The server optimizes a number of client clusters and a dimension of the sketches, subject to a constraint of memory consumption, a constraint of communication overhead, and a performance metric. The server determines a subset of the clients that send the respective gradients, by selecting the clients from the client clusters. The server aggregates the respective gradients sent by the subset of the clients.

CPC Classifications