面向工业物联网可靠被动无线电传感的压缩贝叶斯联邦学习
Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT
AI总结：
针对去中心化贝叶斯联邦学习通信开销高的问题，提出结合后验压缩与多步本地优化的方法，在工业物联网雷达人员定位中保持精度与校准，并将通信量最高降低99%。
摘要：
贝叶斯联邦学习（FL）最近被提出，用于提供经过良好校准的机器学习（ML）模型，以量化其预测的不确定性。尽管与频率派联邦学习设置相比具有优势，但在去中心化网络上实现的贝叶斯联邦学习工具会因协作设备之间反复交换局部后验分布而承受高昂的通信成本。因此，本文提出一种通信高效的去中心化贝叶斯联邦学习策略，以在不牺牲最终学习精度和校准性能的情况下降低通信开销。所提方法集成了压缩策略，并允许设备在发送局部后验分布之前执行多个优化步骤。我们将所开发的工具集成到一个工业物联网（IIoT）用例中：配备自主雷达传感器的协作节点负责在与机器人共享的工作场所中可靠定位人类操作人员。数值结果表明，所开发方法获得了高精度且校准良好的机器学习模型，与传统（未压缩）贝叶斯联邦学习工具提供的模型兼容，同时大幅降低了通信开销（最高可达99%）。此外，在校准方面，尤其当测试数据集的统计分布发生变化时，与最先进的压缩频率派联邦学习设置相比，所提方法具有优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
