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ShapleyFL: Robust Federated Learning Based on Shapley Value

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Kui RenAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 2096 - 2108

https://doi.org/10.1145/3580305.3599500

Published: 04 August 2023 Publication History

Abstract

Federated Learning (FL) allows clients to form a consortium to train a global model under the orchestration of a central server while keeping data on the local client without sharing it, thus mitigating data privacy issues. However, training a robust global model is challenging since the local data is invisible to the server. The local data of clients are naturally heterogeneous, while some clients can use corrupted data or send malicious updates to interfere with the training process artificially. Meanwhile, communication and computation costs are inevitable challenges in designing a practical FL algorithm. In this paper, to improve the robustness of FL, we propose a Shapley value-inspired adaptive weighting mechanism, which regards the FL training as sequential cooperative games and adjusts clients' weights according to their contributions. We also develop a client sampling strategy based on importance sampling, which can reduce the communication cost by optimizing the variance of the global updates according to the weights of clients. Furthermore, to diminish the computation cost of the server, we propose a weight calculation method by estimating differences between the Shapley value of clients. Our experimental results on several real data sets demonstrate the effectiveness of our approaches.

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Cited By

Chen YLi KLi GWang Y(2024)Contributions Estimation in Federated Learning: A Comprehensive Experimental EvaluationProceedings of the VLDB Endowment10.14778/3659437.365945917:8(2077-2090)Online publication date: 31-May-2024
https://dl.acm.org/doi/10.14778/3659437.3659459
Liu YWang CYuan XBaeza-Yates RBonchi F(2024)BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671879(1944-1955)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671879
Li QGao ZSun YWang YWang RZhu H(2024)An Efficient Asynchronous Federated Learning Protocol for Edge DevicesIEEE Internet of Things Journal10.1109/JIOT.2024.340663411:17(28798-28808)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3406634
Show More Cited By

Index Terms

ShapleyFL: Robust Federated Learning Based on Shapley Value
1. Computing methodologies
  1. Distributed computing methodologies
    1. Distributed algorithms

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cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

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Published: 04 August 2023

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

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Cited By

Chen YLi KLi GWang Y(2024)Contributions Estimation in Federated Learning: A Comprehensive Experimental EvaluationProceedings of the VLDB Endowment10.14778/3659437.365945917:8(2077-2090)Online publication date: 31-May-2024
https://dl.acm.org/doi/10.14778/3659437.3659459
Liu YWang CYuan XBaeza-Yates RBonchi F(2024)BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671879(1944-1955)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671879
Li QGao ZSun YWang YWang RZhu H(2024)An Efficient Asynchronous Federated Learning Protocol for Edge DevicesIEEE Internet of Things Journal10.1109/JIOT.2024.340663411:17(28798-28808)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3406634
Bi YWu YLiu JRen KXiong L(2024)When Data Pricing Meets Non-Cooperative Game Theory2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00443(5548-5559)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00443
Liu JXue MLou JZhang XXiong LQin Z(2023)MUter: Machine Unlearning on Adversarially Trained Models2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00451(4869-4879)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.00451

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