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.

采用通信高效的去中心化贝叶斯联邦学习策略，通过集成压缩策略和允许设备在发送本地后验分布之前执行多个优化步骤，以减少通信开销而不牺牲学习准确性和校准，得到与传统（未压缩）贝叶斯联邦学习工具相兼容且高度准确且良好校准的机器学习模型的工业物联网应用实例结果表明，该方法在校准方面具有优势，特别是在测试数据集的统计分布发生变化时。