FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy

arXiv:2310.09789v2 Announce Type: replace
Abstract: Federated learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy. Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data. Nevertheless, the unequal training contributions among clients have made FL vulnerable, as clients with heavily biased datasets can easily compromise FL by sending malicious or …

abstract arxiv cs.lg customers data data privacy devices early-stopping edge edge devices federated learning global intelligent internet internet of things iot orchestration privacy server services strategy train type