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

TL;DR

This paper introduces a communication-efficient decentralized Bayesian Federated Learning method that maintains high accuracy and calibration in IIoT radar sensing applications while significantly reducing communication costs.

Contribution

It proposes a novel compression policy for Bayesian FL that allows multiple local optimization steps, reducing communication without sacrificing model quality.

Findings

Achieves up to 99% reduction in communication overhead.

Maintains high accuracy and calibration comparable to uncompressed Bayesian FL.

Outperforms state-of-the-art compressed frequentist FL in calibration, especially under distribution shifts.

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 calibration. The proposed method integrates compression policies and allows devices to perform multiple optimization steps before sending the local posterior distributions. We integrate the developed tool in an Industrial Internet of Things (IIoT) use case where collaborating nodes equipped with autonomous radar sensors are tasked to reliably localize human operators in a workplace shared with robots. Numerical results show that the developed approach obtains highly accurate yet well-calibrated ML models compatible with the ones provided by conventional (uncompressed) Bayesian FL tools while substantially decreasing the communication overhead (i.e., up to 99%). Furthermore, the proposed approach is advantageous when compared with state-of-the-art compressed frequentist FL setups in terms of calibration, especially when the statistical distribution of the testing dataset changes.