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Multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning

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Algorithms and Architectures for Parallel Processing (ICA3PP 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15254))

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Abstract

Federated Learning (FL) provides a valuable framework that allows for the collaborative training of models across distributed networks while maintaining the privacy of the data involved. The concept of secure aggregation is crucial in preserving both the privacy and integrity of data within FL systems. However, the deployment of secure aggregation encounters significant barriers. Conventional secure aggregation methods are primarily designed for single-round operations, which can become prohibitively expensive in scenarios that require multiple rounds. Additionally, these methods often struggle in large-scale environments characterized by heterogeneous data and models. This paper introduces the Multiple-Round Aggregation of Abstract Semantics (MRAAS), a novel scheme designed to address the aforementioned limitations in secure aggregation within heterogeneous FL environments. The MRAAS scheme enhances security and reduces the need for extensive data exchanges by aggregating abstracted model semantics instead of raw model gradients, facilitating secure, multiple rounds of aggregation. Key innovations of MRAAS include an advanced abstraction technique, robustness against participant drop-out, and the use of recyclable secrets. These innovations collectively enhance system resilience and reduce the necessity for frequent cryptographic renewals. Our theoretical evaluations focus on the security features of MRAAS. Empirical validations conducted through comparative analyses utilizing two public datasets confirm the superiority of MRAAS.

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Acknowledgement

This work was supported by Sichuan Science and Technology Program (2024NSFTD0031, 2024NSFSC0004).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Jiao Zhang & Wei Liang

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xiong Li

Authors
  1. Jiao Zhang
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  2. Xiong Li
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  3. Wei Liang
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Corresponding author

Correspondence to Xiong Li .

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Editors and Affiliations

  1. City University of Macau, Macau, China

    Tianqing Zhu

  2. Guangzhou University, Guangzhou, China

    Jin Li

  3. University of Salerno, Fisciano, Italy

    Aniello Castiglione

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Zhang, J., Li, X., Liang, W. (2025). Multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning. In: Zhu, T., Li, J., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2024. Lecture Notes in Computer Science, vol 15254. Springer, Singapore. https://doi.org/10.1007/978-981-96-1545-2_13

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  • DOI: https://doi.org/10.1007/978-981-96-1545-2_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-96-1544-5

  • Online ISBN: 978-981-96-1545-2

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