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Adaptive client selection and model aggregation for heterogeneous federated learning

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Abstract

Federated Learning (FL) is a distributed machine learning method that allows multiple clients to collaborate on model training without sharing raw data. However, FL faces challenges with data heterogeneity, leading to reduced model accuracy and slower convergence. Although existing client selection methods can alleviate the above problems, there is still room to improve FL performance. To tackle these problems, we first propose a novel client selection method based on Multi-Armed Bandit (MAB). The method uses the historical training information uploaded by each client to calculate its correlation and contribution. The calculated values are then used to select a set of clients that can bring the most benefit, i.e., maximizing both model accuracy and convergence speed. Second, we propose an adaptive global model aggregation method that utilizes the local training information of selected clients to dynamically assign weights to local model parameters. Extensive experiments on various datasets with different heterogeneous settings demonstrate that our proposed method is effectively improving FL performance compared to several benchmarks.

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The data underlying this article is available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (12201185) and the Kaifeng Science and Technology R &D Project (No.2201009).

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Authors and Affiliations

  1. School of Software, Henan University, Kaifeng, 475004, Henan, China

    Rui Zhai, Haozhe Jin, Ke Lu, Yanhong Liu, Yalin Song & Junyang Yu

  2. Henan Provincial Engineering Research Center of Intelligent Data Processing, Henan University, Kaifeng, 475004, Henan, China

    Rui Zhai, Haozhe Jin, Yanhong Liu, Yalin Song & Junyang Yu

  3. School of Engineering Science, University of Chinese Academy of Sciences, Beijing, 100049, China

    Ke Lu

  4. Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

    Wei Gong

  5. Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, 201210, China

    Wei Gong

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  1. Rui Zhai
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Contributions

R. Zhai, H. Jin, and K. Lu contributed to the study conception and design. R. Zhai and H. Jin conducted experiments and wrote the initial manuscript. R. Zhai, W. Gong, and K. Lu reviewed and edited it. K. Lu, Y. Liu, Y. Song, and J. Yu supervised the manuscript writing progress. All authors reviewed the manuscript.

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Correspondence to Ke Lu.

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Communicated by Teng Li.

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Zhai, R., Jin, H., Gong, W. et al. Adaptive client selection and model aggregation for heterogeneous federated learning. Multimedia Systems 30, 211 (2024). https://doi.org/10.1007/s00530-024-01386-w

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