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Federated Learning with GAN-Based Data Synthesis for Non-IID Clients

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Trustworthy Federated Learning (FL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13448))

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Abstract

Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distributed (non-IID) data among clients. In this chapter, we propose a novel framework, named Synthetic Data Aided Federated Learning (SDA-FL), to resolve this non-IID challenge by sharing synthetic data. Specifically, each client pretrains a local generative adversarial network (GAN) to generate differentially private synthetic data, which are uploaded to the parameter server (PS) to construct a global shared synthetic dataset. To generate confident pseudo labels for the synthetic dataset, we also propose an iterative pseudo labeling mechanism performed by the PS. The assistance of the synthetic dataset with confident pseudo labels significantly alleviates the data heterogeneity among clients, which improves the consistency among local updates and benefits the global aggregation. Extensive experiments evidence that the proposed framework outperforms the baseline methods by a large margin in several benchmark datasets under both the supervised and semi-supervised settings.

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Author information

Authors and Affiliations

  1. The Hong Kong University of Science and Technology, Hong Kong, China

    Zijian Li, Jiawei Shao & Jun Zhang

  2. The Hong Kong Polytechnic University, Hong Kong, China

    Yuyi Mao

  3. Tsinghua University, Beijing, China

    Jessie Hui Wang

Authors
  1. Zijian Li
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  2. Jiawei Shao
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  3. Yuyi Mao
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  4. Jessie Hui Wang
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  5. Jun Zhang
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Corresponding author

Correspondence to Zijian Li .

Editor information

Editors and Affiliations

  1. University of Alberta, Edmonton, AB, Canada

    Randy Goebel

  2. Nanyang Technological University, Singapore, Singapore

    Han Yu

  3. École polytechnique fédérale de Lausanne, Lausanne, Switzerland

    Boi Faltings

  4. WeBank, Shenzhen, China

    Lixin Fan

  5. Singapore University of Technology and Design, Singapore, Singapore

    Zehui Xiong

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Li, Z., Shao, J., Mao, Y., Wang, J.H., Zhang, J. (2023). Federated Learning with GAN-Based Data Synthesis for Non-IID Clients. In: Goebel, R., Yu, H., Faltings, B., Fan, L., Xiong, Z. (eds) Trustworthy Federated Learning. FL 2022. Lecture Notes in Computer Science(), vol 13448. Springer, Cham. https://doi.org/10.1007/978-3-031-28996-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-28996-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28995-8

  • Online ISBN: 978-3-031-28996-5

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