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Grouped federated learning for time-sensitive tasks in industrial IoTs

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Abstract

The Industrial Internet of Things (IoT) is a crucial part of Industry 4.0 and constantly calls for intelligence to improve productivity. Meanwhile, the federated learning scheme has recently been proposed to provide both distributed learning ability and inherent privacy protection functions. Despite its original advantages, the unique features of industrial IoT posed several challenges to federated learning regarding efficiency and scalability. Due to the heterogeneity of data under IoT and the large difference in computing efficiency of each device, the training time of federated learning will be affected by stragglers. To this end, an effective device selection mechanism is urgently needed to improve the training efficiency of global model. Additionally, the communication coordination problem among various devices should be considered to accelerate global convergence. To solve the abovementioned problems, we proposed an Upper Confidence Bound (UCB)-based grouping federated learning (UGFL) where a data scheduling method effectively reduces the stragglers. We combined Lyapunov optimization with the UCB (Garivier and Moulines in International Conference on Algorithmic Learning Theory, pp. 178–188, 2011) algorithm to design an efficient device selection method to choose the best device to participate in each global epoch. In addition, we theoretically analyzed the upper bound of convergence of our proposed method. Experimental results show that our method has higher accuracy and faster convergence speed than the existing methods.

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The data used in the paper are all public datasets.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant (No. 62172215), in part by the Natural Science Foundation of Jiangsu Province (No. BK20200067), in part by the A3 Foresight Program of NSFC (Grant No. 62061146002), in part by Science and Technology Project of Sichuan Province (2023YFG0112) and in part by Opening Fund of Power Internet of Things Key Laboratory of Sichuan Province (No. PIT-F-202203).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangjun Avenue, Nanjing, 211106, Jiangsu, China

    Jiangshan Hao & Yanchao Zhao

  2. Power Internet of Things Key Laboratory, State Grid Sichuan Electric Power Research Institute, Jinhui West Second Street, Chengdu, 610094, Sichuan, China

    Linghao Zhang & Yanchao Zhao

Authors
  1. Jiangshan Hao
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  2. Linghao Zhang
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  3. Yanchao Zhao
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Contributions

Jiangshan Hao wrote the main content of the manuscript. Yanchao Zhao provided the writing ideas for the paper and revised the paper. Linghao Zhang checked the experimental results and drew the system flowchart.

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Correspondence to Yanchao Zhao.

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This article is part of the Topical Collection: 4 - Track on IoT

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Hao, J., Zhang, L. & Zhao, Y. Grouped federated learning for time-sensitive tasks in industrial IoTs. Peer-to-Peer Netw. Appl. 17, 819–833 (2024). https://doi.org/10.1007/s12083-023-01616-4

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