Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning maintenance engineers of impending failure or identifying current system health conditions. However, single operators may not have sufficiently large fleets of systems or component units to collect sufficient data to develop data-driven algorithms. Collecting a satisfactory quantity of fault patterns for safety-critical systems is particularly difficult due to the rarity of faults. Federated learning (FL) has emerged as a promising solution to leverage datasets from multiple operators to train a decentralized asset fault diagnosis model while maintaining data confidentiality. However, there are still considerable obstacles to overcome when it comes to optimizing the federation strategy without leaking sensitive data and addressing the issue of client dataset heterogeneity. This is particularly prevalent in fault diagnosis applications due to the high diversity of operating conditions and system configurations. To address these two challenges, we propose a novel clustering-based FL algorithm where clients are clustered for federating based on dataset similarity. To quantify dataset similarity between clients without explicitly sharing data, each client sets aside a local test dataset and evaluates the other clients' model prediction accuracy and uncertainty on this test dataset. Clients are then clustered for FL based on relative prediction accuracy and uncertainty.

翻译：各行业运营商正推动无线传感节点在工业监测中的应用，相关努力已产生大量状态监测数据集，这些数据可用于构建能够预警维修工程师即将发生的故障或识别当前系统健康状态的诊断算法。然而，单个运营商可能没有足够规模的系统或部件单元来收集充足数据以开发数据驱动算法。对于安全关键系统而言，由于故障模式罕见，收集足够数量的故障样本尤为困难。联邦学习（FL）作为一种有前景的解决方案，可利用多个运营商的数据集训练分布式资产故障诊断模型，同时保持数据保密性。然而，在优化联邦策略时避免泄露敏感数据并解决客户端数据集异构性问题仍面临重大障碍。这一问题在故障诊断应用中尤为突出，因为运行条件和系统配置的高度多样性。为应对这两个挑战，我们提出了一种基于聚类的联邦学习算法，该算法根据数据集相似性对客户端进行分组联邦。为在不直接共享数据的情况下量化客户端间的数据集相似性，每个客户端保留本地测试数据集，并评估其他客户端模型在该测试数据集上的预测准确性和不确定性。随后，基于相对预测准确性和不确定性对客户端进行联邦聚类。