More Web Proxy on the site http://driver.im/

research-article

Free access

ShapleyFL: Robust Federated Learning Based on Shapley Value

Authors:

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.

Supplementary Material

MP4 File (rtfp1446-2min-promo.mp4)

Promotional video

Download
4.04 MB

References

[1]

Ergute Bao, Yizheng Zhu, Xiaokui Xiao, Yin Yang, Beng Chin Ooi, Benjamin Hong Meng Tan, and Khin Mi Mi Aung. 2022. Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy. Proc. VLDB Endow., Vol. 15, 11 (2022), 2348--2360. https://www.vldb.org/pvldb/vol15/p2348-bao.pdf

Digital Library

[2]

Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017. Machine learning with adversaries: Byzantine tolerant gradient descent. Advances in Neural Information Processing Systems, Vol. 30 (2017).

[3]

Christopher Briggs, Zhong Fan, and Peter Andras. 2020. Federated learning with hierarchical clustering of local updates to improve training on non-IID data. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--9.

[4]

Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. 2019. Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. In Advances in Neural Information Processing Systems, Vol. 32. Curran Associates, Inc.

[5]

Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong. 2020. Fltrust: Byzantine-robust federated learning via trust bootstrapping. arXiv preprint arXiv:2012.13995 (2020).

[6]

Javier Castro, Daniel Gó mez, and Juan Tejada. 2009. Polynomial calculation of the Shapley value based on sampling. Computers & OR, Vol. 36, 5 (2009), 1726--1730.

Digital Library

[7]

Wenlin Chen, Samuel Horvath, and Peter Richtarik. 2020. Optimal client sampling for federated learning. arXiv preprint arXiv:2010.13723 (2020).

[8]

Yae Jee Cho, Jianyu Wang, and Gauri Joshi. 2022. Towards understanding biased client selection in federated learning. In International Conference on Artificial Intelligence and Statistics. PMLR, 10351--10375.

[9]

Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, et al. 2018. Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, 168--172.

[10]

Xiaotie Deng and Christos H Papadimitriou. 1994. On the complexity of cooperative solution concepts. Mathematics of operations research, Vol. 19, 2 (1994), 257--266.

[11]

Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, et al. [n.d.]. FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.

[12]

Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P Friedlander, Changxin Liu, and Yong Zhang. 2022. Improving Fairness for Data Valuation in Horizontal Federated Learning. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2440--2453.

[13]

Fangcheng Fu, Xupeng Miao, Jiawei Jiang, Huanran Xue, and Bin Cui. 2022. Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update. Proc. VLDB Endow., Vol. 15, 10 (2022), 2111--2120. https://www.vldb.org/pvldb/vol15/p2111-fu.pdf

Digital Library

[14]

Amirata Ghorbani and James Zou. 2019. Data shapley: Equitable valuation of data for machine learning. In International Conference on Machine Learning. PMLR, 2242--2251.

[15]

Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran. 2020. An efficient framework for clustered federated learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 19586--19597.

[16]

Yufei Han and Xiangliang Zhang. 2020. Robust federated learning via collaborative machine teaching. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 4075--4082.

[17]

Samuel Horváth and Peter Richtárik. 2019. Nonconvex variance reduced optimization with arbitrary sampling. In International Conference on Machine Learning. PMLR, 2781--2789.

[18]

Zhida Jiang, Yang Xu, Hongli Xu, Zhiyuan Wang, Chunming Qiao, and Yangming Zhao. 2022. FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing. In 38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9-12, 2022. IEEE, 767--779. https://doi.org/10.1109/ICDE53745.2022.00062

[19]

Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, and Dejing Dou. 2022. Accelerated Federated Learning with Decoupled Adaptive Optimization. In International Conference on Machine Learning. PMLR, 10298--10322.

[20]

Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2021. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, Vol. 14, 1--2 (2021), 1--210.

[21]

Hamed Karimi, Julie Nutini, and Mark Schmidt. 2016. Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition. In Joint European conference on machine learning and knowledge discovery in databases. Springer, 795--811.

Digital Library

[22]

Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. 2020. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning. PMLR, 5132--5143.

[23]

Junyi Li, Jian Pei, and Heng Huang. 2022b. Communication-Efficient Robust Federated Learning with Noisy Labels. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 914--924.

Digital Library

[24]

Qinbin Li, Yiqun Diao, Quan Chen, and Bingsheng He. 2022a. Federated Learning on Non-IID Data Silos: An Experimental Study. In 38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9-12, 2022. IEEE, 965--978. https://doi.org/10.1109/ICDE53745.2022.00077

[25]

Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020a. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, Vol. 37, 3 (2020), 50--60.

[26]

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020b. Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, Vol. 2 (2020), 429--450.

[27]

Zengpeng Li, Vishal Sharma, and Saraju P Mohanty. 2020c. Preserving data privacy via federated learning: Challenges and solutions. IEEE Consumer Electronics Magazine, Vol. 9, 3 (2020), 8--16.

[28]

Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, and Xiaofeng Meng. 2021. Projected Federated Averaging with Heterogeneous Differential Privacy. Proc. VLDB Endow., Vol. 15, 4 (2021), 828--840. https://doi.org/10.14778/3503585.3503592

Digital Library

[29]

Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu, and Lizhen Cui. 2022. GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 13, 4 (2022), 1--21.

Digital Library

[30]

Bing Luo, Xiang Li, Shiqiang Wang, Jianwei Huang, and Leandros Tassiulas. 2021. Cost-effective federated learning design. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications. IEEE, 1--10.

Digital Library

[31]

Bing Luo, Wenli Xiao, Shiqiang Wang, Jianwei Huang, and Leandros Tassiulas. 2022. Tackling system and statistical heterogeneity for federated learning with adaptive client sampling. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications. IEEE, 1739--1748.

Digital Library

[32]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR, 1273--1282.

[33]

Lokesh Nagalapatti and Ramasuri Narayanam. 2021. Game of gradients: Mitigating irrelevant clients in federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 9046--9054.

[34]

Takayuki Nishio and Ryo Yonetani. 2019. Client selection for federated learning with heterogeneous resources in mobile edge. In ICC 2019--2019 IEEE international conference on communications (ICC). IEEE, 1--7.

[35]

Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui. 2022. Robust aggregation for federated learning. IEEE Transactions on Signal Processing, Vol. 70 (2022), 1142--1154.

[36]

Shashank Rajput, Hongyi Wang, Zachary Charles, and Dimitris Papailiopoulos. 2019. DETOX: A redundancy-based framework for faster and more robust gradient aggregation. Advances in Neural Information Processing Systems, Vol. 32 (2019).

[37]

Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N Galtier, Bennett A Landman, Klaus Maier-Hein, et al. 2020. The future of digital health with federated learning. NPJ digital medicine, Vol. 3, 1 (2020), 1--7.

[38]

Xinyi Shang, Yang Lu, Yiu-ming Cheung, and Hanzi Wang. 2022. FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation. arXiv preprint arXiv:2205.00172 (2022).

[39]

Lloyd S Shapley. 1953. A value for n-person games. Contributions to the Theory of Games, Vol. 2, 28 (1953), 307--317.

[40]

Xian Shuai, Yulin Shen, Siyang Jiang, Zhihe Zhao, Zhenyu Yan, and Guoliang Xing. 2022. BalanceFL: Addressing Class Imbalance in Long-Tail Federated Learning. In 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 271--284.

[41]

Farnaz Tahmasebian, Jian Lou, and Li Xiong. 2022. Robustfed: a truth inference approach for robust federated learning. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1868--1877.

Digital Library

[42]

Philipp Tschandl, Cliff Rosendahl, and Harald Kittler. 2018. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data, Vol. 5, 1 (2018), 1--9.

[43]

Guan Wang, Charlie Xiaoqian Dang, and Ziye Zhou. 2019. Measure contribution of participants in federated learning. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2597--2604.

[44]

Tianhao Wang, Johannes Rausch, Ce Zhang, Ruoxi Jia, and Dawn Song. 2020. A principled approach to data valuation for federated learning. In Federated Learning. Springer, 153--167.

[45]

Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, and Shu-Tao Xia. 2018. Iterative learning with open-set noisy labels. In Proceedings of the IEEE conference on computer vision and pattern recognition. 8688--8696.

[46]

Hongda Wu and Ping Wang. 2021. Fast-convergent federated learning with adaptive weighting. IEEE Transactions on Cognitive Communications and Networking, Vol. 7, 4 (2021), 1078--1088.

[47]

Yuncheng Wu, Shaofeng Cai, Xiaokui Xiao, Gang Chen, and Beng Chin Ooi. 2020. Privacy Preserving Vertical Federated Learning for Tree-based Models. Proc. VLDB Endow., Vol. 13, 11 (2020), 2090--2103. http://www.vldb.org/pvldb/vol13/p2090-wu.pdf

Digital Library

[48]

Jian Xu, Shao-Lun Huang, Linqi Song, and Tian Lan. 2022. Byzantine-robust federated learning through collaborative malicious gradient filtering. In 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS). IEEE, 1223--1235.

[49]

Miao Yang, Hua Qian, Ximin Wang, Yong Zhou, and Hongbin Zhu. 2021. Client Selection for Federated Learning With Label Noise. IEEE Transactions on Vehicular Technology, Vol. 71, 2 (2021), 2193--2197.

[50]

Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. 2018. Byzantine-robust distributed learning: Towards optimal statistical rates. In International Conference on Machine Learning. PMLR, 5650--5659.

[51]

Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, and Yuan Gao. 2021. A survey on federated learning. Knowledge-Based Systems, Vol. 216 (2021), 106775.

[52]

Jiayao Zhang, Qiheng Sun, Jinfei Liu, Li Xiong, Jian Pei, and Kui Ren. 2023. Efficient Sampling Approaches to Shapley Value Approximation. In SIGMOD. ACM.

[53]

Shuyuan Zheng, Yang Cao, and Masatoshi Yoshikawa. 2022. Secure Shapley Value for Cross-Silo Federated Learning. arXiv preprint arXiv:2209.04856 (2022).

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
Sun PWu LWang ZLiu JLuo JJin W(2024)A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price CompetitionProceedings of the ACM on Management of Data10.1145/36771272:4(1-27)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677127
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
Show More Cited By

Index Terms

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

Recommendations

Enhancing Federated Learning: A Novel Approach of Shapley Value Computation in Smart Contract
Advanced Intelligent Computing Technology and Applications
Abstract
In federated learning (FL), the success of model training largely hinges on the contributions from clients. Current FL frameworks encounter obstacles in pinpointing and compensating high-contribution clients effectively. This paper proposes a ...
Affordable federated edge learning framework via efficient Shapley value estimation
Abstract
Federated Learning (FL), as a privacy-preserving distributed machine learning paradigm, has become a promising privacy computing framework for increasingly complex network systems. To incentivize more data owners to participate, it is important ...
Highlights
- Federated Shapley Value (FedSV): an affordable federated edge learning framework is proposed.
- The formal methods, and application scenarios of FedSV are provided.
- Analyzing core challenges in terms of complexity and performance.
Optimizing Federated Learning on Non-IID Data Using Local Shapley Value
Artificial Intelligence
Abstract
Federated learning (FL) was originally proposed as a new distributed machine learning paradigm that addresses the data security and privacy protection issues with a global model trained by ubiquitous local data. Currently, FL techniques have been ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

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

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 August 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Major Programs of the National Social Science Foundation of China
NIH grants
National Key R&D Program of China
NSF grants
NSFC grants

Conference

KDD '23

Sponsor:

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

August 6 - 10, 2023

CA, Long Beach, USA

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
1,946
Total Downloads

Downloads (Last 12 months)1,327
Downloads (Last 6 weeks)154

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

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
Sun PWu LWang ZLiu JLuo JJin W(2024)A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price CompetitionProceedings of the ACM on Management of Data10.1145/36771272:4(1-27)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677127
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
Huang WYe MShi ZWan GLi HDu BYang Q(2024)Federated Learning for Generalization, Robustness, Fairness: A Survey and BenchmarkIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.341886246:12(9387-9406)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3418862
Wu LGuo SDing YWang JXu WZhan YKermarrec A(2024)Rethinking Personalized Client Collaboration in Federated LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.339621823:12(11227-11239)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3396218
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
Li JChen TTeng S(2024)A comprehensive survey on client selection strategies in federated learningComputer Networks10.1016/j.comnet.2024.110663251(110663)Online publication date: Sep-2024
https://doi.org/10.1016/j.comnet.2024.110663
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

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents