Federated Learning (FL) is the most widely adopted collaborative learning approach for training decentralized Machine Learning (ML) models by exchanging learning between clients without sharing the data and compromising privacy. However, since great data similarity or homogeneity is taken for granted in all FL tasks, FL is still not specifically designed for the industrial setting. Rarely this is the case in industrial data because there are differences in machine type, firmware version, operational conditions, environmental factors, and hence, data distribution. Albeit its popularity, it has been observed that FL performance degrades if the clients have heterogeneous data distributions. Therefore, we propose a Lightweight Industrial Cohorted FL (LICFL) algorithm that uses model parameters for cohorting without any additional on-edge (clientlevel) computations and communications than standard FL and mitigates the shortcomings from data heterogeneity in industrial applications. Our approach enhances client-level model performance by allowing them to collaborate with similar clients and train more specialized or personalized models. Also, we propose an adaptive aggregation algorithm that extends the LICFL to Adaptive LICFL (ALICFL) for further improving the global model performance and speeding up the convergence. Through numerical experiments on real-time data, we demonstrate the efficacy of the proposed algorithms and compare the performance with existing approaches.

翻译：联邦学习（Federated Learning, FL）是当前应用最广泛的协同学习方法，它通过在客户端之间交换学习成果（而非共享数据）来训练去中心化的机器学习（Machine Learning, ML）模型，从而保护数据隐私。然而，由于所有FL任务都默认数据具有高度相似性或同质性，现有FL方法并未针对工业场景进行专门设计。这在工业数据中却很少见，因为机器类型、固件版本、运行条件、环境因素等方面存在差异，从而导致数据分布不同。尽管FL广受欢迎，但已有研究发现，若客户端的数据分布具有异构性，FL的性能会下降。因此，我们提出了一种轻量级工业群组联邦学习（Lightweight Industrial Cohorted FL, LICFL）算法。该算法利用模型参数进行群组划分，无需在边缘（客户端）进行任何超出标准FL的额外计算和通信，从而缓解了工业应用中数据异构性带来的缺陷。我们的方法通过允许客户端与相似的客户端协作并训练更专门化或个性化的模型，从而提升了客户端层面的模型性能。此外，我们还提出了一种自适应聚合算法，将LICFL扩展为自适应LICFL（Adaptive LICFL, ALICFL），以进一步提升全局模型性能并加速收敛。通过在实时数据上进行数值实验，我们验证了所提算法的有效性，并与现有方法进行了性能比较。