Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

• URL: http://arxiv.org/abs/2202.01512v1
• Date: Thu, 3 Feb 2022 10:48:17 GMT
• Title: Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
• Authors: Zonghang Li, Yihong He, Hongfang Yu, Jiawen Kang, Xiaoping Li, Zenglin Xu, Dusit Niyato
• Abstract summary: FedGS is a hierarchical cloud-edge-end FL framework for 5G empowered industries. Taking advantage of naturally clustered factory devices, FedGS uses a gradient-based binary permutation algorithm. Experiments show that FedGS improves accuracy by 3.5% and reduces training rounds by 59% on average.
• Score: 57.67687126339891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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