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FRAIM: A Feature Importance-Aware Incentive Mechanism for Vertical Federated Learning

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Algorithms and Architectures for Parallel Processing (ICA3PP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14491))

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Abstract

Federated learning, a new distributed learning paradigm, has the advantage of sharing model information without revealing data privacy. However, considering the selfishness of organizations, they will not participate in federated learning without compensation. To address this problem, in this paper, we design a feature importance-aware vertical federated learning incentive mechanism. We first synthesize a small amount of data locally using the interpolation method at the organization and send it to the coordinator for evaluating the contribution of each feature to the learning task. Then, the coordinator calculates the importance value of each feature in the dataset for the current task using the Shapley value method according to the synthetic data. Next, we formulate the process of organization participation in the federation as a feature importance maximization problem based on reverse auction which is a knapsack auction problem. Finally, we design an approximate algorithm to solve the proposed optimization problem and the solution of the approximation algorithm is shown to be \(\frac{1}{2}\)-approximate to the optimal solution. Furthermore, we prove that the proposed mechanism is truthfulness, individual rationality, and computational efficiency. The superiority of our proposed mechanism is verified through experiments on real-world datasets.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant U1911201, U2001209, the Natural Science Foundation of Guangdong under Grant 2021A1515011369, and the Science and Technology Program of Guangzhou under Grant 2023A04J2029.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China

    Lei Tan, Yunchao Yang, Miao Hu & Di Wu

  2. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China

    Lei Tan, Yunchao Yang, Miao Hu & Di Wu

  3. School of Computing, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia

    Yipeng Zhou

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  1. Lei Tan
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  2. Yunchao Yang
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  3. Miao Hu
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  4. Yipeng Zhou
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  5. Di Wu
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Corresponding author

Correspondence to Di Wu .

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Editors and Affiliations

  1. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Zahir Tari

  2. Tianjin University, Tianjin, China

    Keqiu Li

  3. University of Arizona, Tucson, AZ, USA

    Hongyi Wu

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Tan, L., Yang, Y., Hu, M., Zhou, Y., Wu, D. (2024). FRAIM: A Feature Importance-Aware Incentive Mechanism for Vertical Federated Learning. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14491. Springer, Singapore. https://doi.org/10.1007/978-981-97-0808-6_8

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  • DOI: https://doi.org/10.1007/978-981-97-0808-6_8

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  • Print ISBN: 978-981-97-0807-9

  • Online ISBN: 978-981-97-0808-6

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