FedFisher: Leveraging Fisher Information for One-Shot Federated Learning

arXiv:2403.12329v1 Announce Type: new
Abstract: Standard federated learning (FL) algorithms typically require multiple rounds of communication between the server and the clients, which has several drawbacks, including requiring constant network connectivity, repeated investment of computational resources, and susceptibility to privacy attacks. One-Shot FL is a new paradigm that aims to address this challenge by enabling the server to train a global model in a single round of communication. In this work, we present FedFisher, a novel algorithm for one-shot FL …

abstract algorithms arxiv attacks communication computational connectivity cs.dc cs.lg federated learning fisher information investment multiple network new paradigm paradigm privacy resources server standard stat.ml type