ACCELERATING LOCAL TRAINING AND ACHIEVING A HIGHLY ACCURATE GLOBAL MODEL FOR FEDERATED LEARNING (FL)

Abstract

Global gradients of a global model from a server are received at a plurality of device. Aggressive regularization-based layer freezing is applied at the plurality of devices to the global gradients to identify local layers to freeze in a local model. Based on the local layers identified to freeze, a local state list of the local model is produced. Local gradients produced by the plurality of devices are received at the server. Global gradients are created at the server based on the local gradients. Conservative convergence-based layer freezing is applied at the server to produce a list of frozen layers of the global model based on the global gradients. The list of frozen layers of the global model are provided to the plurality of devices for producing the local state list.

CPC Classifications