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A federated learning model based on filtering strategy

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Abstract

With the increase of IoT terminals, data has also shown explosive growth in recent years. Due to the dispersion, low replication cost, and value aggregation of data, data owners are unwilling to share data. Therefore, it is difficult for them to use each other’s data for analysis or modeling, and the problem of data islands is serious. Federated learning can solve this problem. It uses the parameter data provided by different nodes to complete the training of the same global model, which greatly ensures the security and privacy of the data. However, if there are malicious nodes, the accuracy of global model training will be significantly reduced, and it will cause an unnecessary communication burden. Therefore, this paper proposes a federated learning model based on filtering strategy. The model filters three types of malicious nodes through the filtering algorithm to ensure the accuracy of the global model. The reward and punishment mechanism are used to reduce the weight of malicious participating nodes to have fewer opportunities to participate in training and reduce unnecessary communication overhead. The experiments show that the model has an excellent filtering effect on three types of malicious nodes.

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Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, 2 Beinong Road, 102206, Beijing, China

    Jingchen Yan, Mingyu Sun, Zhe Du, Xin Tong & Jianbin Li

  2. First System Department, North China Institute of Computing Technology, 269 Beijing North Fourth Ring Road, 100083, Beijing, China

    Jingchen Yan

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  1. Jingchen Yan
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  2. Mingyu Sun
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  3. Zhe Du
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  4. Xin Tong
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  5. Jianbin Li
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Correspondence to Jianbin Li.

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No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere. All the authors listed have approved the manuscript that is enclosed.

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Yan, J., Sun, M., Du, Z. et al. A federated learning model based on filtering strategy. World Wide Web 26, 1031–1053 (2023). https://doi.org/10.1007/s11280-022-01074-7

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