Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT

arXiv:2405.05855v1 Announce Type: new
Abstract: Bayesian Federated Learning (FL) has been recently introduced to provide well-calibrated Machine Learning (ML) models quantifying the uncertainty of their predictions. Despite their advantages compared to frequentist FL setups, Bayesian FL tools implemented over decentralized networks are subject to high communication costs due to the iterated exchange of local posterior distributions among cooperating devices. Therefore, this paper proposes a communication-efficient decentralized Bayesian FL policy to reduce the communication overhead without sacrificing final learning accuracy and …

abstract advantages arxiv bayesian communication costs cs.dc cs.lg decentralized federated learning industrial industrial iot iot machine machine learning networks predictions radio sensing tools type uncertainty