Nowadays, the industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate on decentralized devices in modern factories. To protect the confidentiality of industrial data, federated learning (FL) was introduced to collaboratively train shared machine learning models. However, the local data collected by different devices skew in class distribution and degrade industrial FL performance. This challenge has been widely studied at the mobile edge, but they ignored the rapidly changing streaming data and clustering nature of factory devices, and more seriously, they may threaten data security. In this paper, we propose FedGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-i.i.d. data. Taking advantage of naturally clustered factory devices, FedGS uses a gradient-based binary permutation algorithm (GBP-CS) to select a subset of devices within each factory and build homogeneous super nodes participating in FL training. Then, we propose a compound-step synchronization protocol to coordinate the training process within and among these super nodes, which shows great robustness against data heterogeneity. The proposed methods are time-efficient and can adapt to dynamic environments, without exposing confidential industrial data in risky manipulation. We prove that FedGS has better convergence performance than FedAvg and give a relaxed condition under which FedGS is more communication-efficient. Extensive experiments show that FedGS improves accuracy by 3.5% and reduces training rounds by 59% on average, confirming its superior effectiveness and efficiency on non-i.i.d. data.

本文提出了一个基于云边端的联邦学习框架FedGS，利用分层结构和梯度二元置换算法选择工厂内的子设备，构建同质化的超级节点参与联邦学习训练，采用复合步骤同步协议协调这些超级节点内部和之间的培训过程，以提高在非独立同分布数据上的工业联邦学习性能，实验表明，FedGS相对于传统方法在提高准确性上有着更高的效率和效果，同时可以适应动态的环境，同时确保了数据的安全性。

工业物联网中基于组客户端选择的数据异构鲁棒联邦学习