research-article

Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction

Authors:

Rong PanAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 947 - 956

https://doi.org/10.1145/3442381.3449888

Published: 03 June 2021 Publication History

Abstract

Current research on federated learning mainly focuses on joint optimization, improving efficiency and effectiveness, and protecting privacy. However, there are relatively few studies on incentive mechanisms. Most studies fail to consider the fact that if there is no profit, participants have no incentive to provide data and training models, and task requesters cannot identify and select reliable participants with high-quality data. Therefore, this paper proposes a federated learning incentive mechanism based on reputation and reverse auction theory. Participants bid for tasks, and reputation indirectly reflects their reliability and data quality. In this federated learning program, we select and reward participants by combining the reputation and bids of the participants under a limited budget. Theoretical analysis proves that the mechanism satisfies computational efficiency, individual rationality, budget feasibility, and truthfulness. The simulation results show the effectiveness of the mechanism.

References

[1]

Anish Agarwal, Munther A Dahleh, and Tuhin Sarkar. 2019. A Marketplace for Data: An Algorithmic Solution. (2019), 701–726.

[2]

Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020. How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics. PMLR, 2938–2948.

[3]

Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984(2018).

[4]

Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 1175–1191.

Digital Library

[5]

Sebastian Caldas, Jakub Konečny, H Brendan McMahan, and Ameet Talwalkar. 2018. Expanding the reach of federated learning by reducing client resource requirements. arXiv preprint arXiv:1812.07210(2018).

[6]

Mahawaga Arachchige Pathum Chamikara, Peter Bertok, Ibrahim Khalil, Dongxi Liu, and Seyit Camtepe. 2020. Privacy Preserving Distributed Machine Learning with Federated Learning. arXiv preprint arXiv:2004.12108(2020).

[7]

Yeon-Koo Che. 1993. Design competition through multidimensional auctions. The RAND Journal of Economics(1993), 668–680.

[8]

Mingshu Cong, Han Yu, Xi Weng, Jiabao Qu, Yang Liu, and Siu Ming Yiu. 2020. A VCG-based Fair Incentive Mechanism for Federated Learning. arXiv preprint arXiv:2008.06680(2020).

[9]

Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557(2017).

[10]

Benjamin Gompertz. 1825. XXIV. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. In a letter to Francis Baily, Esq. FRS &c. Philosophical transactions of the Royal Society of London115 (1825), 513–583.

[11]

Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604(2018).

[12]

Kuan Lun Huang, Salil S Kanhere, and Wen Hu. 2014. On the need for a reputation system in mobile phone based sensing. Ad Hoc Networks 12(2014), 130–149.

Digital Library

[13]

Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nicholas Hynes, Nezihe Merve Gurel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos. 2019. Towards Efficient Data Valuation Based on the Shapley Value. 89 (2019), 1167–1176.

[14]

Yutao Jiao, Ping Wang, Dusit Niyato, Bin Lin, and Dong In Kim. 2020. Toward an Automated Auction Framework for Wireless Federated Learning Services Market. IEEE Transactions on Mobile Computing(2020).

Digital Library

[15]

Jiawen Kang, Zehui Xiong, Dusit Niyato, Shengli Xie, and Junshan Zhang. 2019. Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory. IEEE Internet of Things Journal 6, 6 (2019), 10700–10714.

[16]

J. Kang, Z. Xiong, D. Niyato, H. Yu, Y. Liang, and D. I. Kim. 2019. Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach. In 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS). 1–5.

[17]

Latif U Khan, Nguyen H Tran, Shashi Raj Pandey, Walid Saad, Zhu Han, Minh NH Nguyen, and Choong Seon Hong. 2019. Federated learning for edge networks: Resource optimization and incentive mechanism. arXiv preprint arXiv:1911.05642(2019).

[18]

Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik. 2016. Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527(2016).

[19]

Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016a. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492(2016a).

[20]

Juong-Sik Lee and Baik Hoh. 2010. Sell your experiences: a market mechanism based incentive for participatory sensing. In 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 60–68.

[21]

T. Li, A. K. Sahu, A. Talwalkar, and V. Smith. 2020. Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine 37, 3 (2020), 50–60.

[22]

W. Liu, L. Chen, Y. Chen, and W. Zhang. 2020. Accelerating Federated Learning via Momentum Gradient Descent. IEEE Transactions on Parallel and Distributed Systems 31, 8 (2020), 1754–1766.

[23]

Kalikinkar Mandal, Guang Gong, and Chuyi Liu. 2018. Nike-based fast privacy-preserving high-dimensional data aggregation for mobile devices. Technical Report. CACR Technical Report, CACR2018–10, University of Waterloo, Canada.

[24]

H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, 2016. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629(2016).

[25]

H Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Aguera y Arcas. 2016. Federated learning of deep networks using model averaging. (2016).

[26]

Roger B Myerson. 1981. Optimal auction design. Mathematics of operations research 6, 1 (1981), 58–73.

[27]

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.

[28]

Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The pagerank citation ranking: Bringing order to the web.Technical Report. Stanford InfoLab.

[29]

Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays. 2019. Federated learning for emoji prediction in a mobile keyboard. arXiv preprint arXiv:1906.04329(2019).

[30]

Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani. 2020. Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization. In International Conference on Artificial Intelligence and Statistics. 2021–2031.

[31]

F. Sattler, K. Müller, and W. Samek. 2020. Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints. IEEE Transactions on Neural Networks and Learning Systems (2020), 1–13.

[32]

F. Sattler, S. Wiedemann, K. R. Müller, and W. Samek. 2020. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data. IEEE Transactions on Neural Networks and Learning Systems 31, 9(2020), 3400–3413.

[33]

Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan. 2019. Can You Really Backdoor Federated Learning?arXiv preprint arXiv:1911.07963(2019).

[34]

Nguyen H Tran, Wei Bao, Albert Zomaya, Nguyen Minh NH, and Choong Seon Hong. 2019. Federated learning over wireless networks: Optimization model design and analysis. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 1387–1395.

Digital Library

[35]

Guan Wang. 2019. Interpret federated learning with shapley values. arXiv preprint arXiv:1905.04519(2019).

[36]

Jianyu Wang and Gauri Joshi. 2018. Cooperative SGD: A unified framework for the design and analysis of communication-efficient SGD algorithms. arXiv preprint arXiv:1808.07576(2018).

[37]

Yufeng Wang, Xueyu Jia, Qun Jin, and Jianhua Ma. 2016. QuaCentive: a quality-aware incentive mechanism in mobile crowdsourced sensing (MCS). The Journal of Supercomputing 72, 8 (2016), 2924–2941.

Digital Library

[38]

Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi. 2019. Beyond inferring class representatives: User-level privacy leakage from federated learning. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2512–2520.

Digital Library

[39]

Jia Xu, Weiwei Bao, Huayue Gu, Lijie Xu, and Guoping Jiang. 2018. Improving both quantity and quality: Incentive mechanism for social mobile crowdsensing architecture. IEEE Access 6(2018), 44992–45003.

[40]

D. Yang, G. Xue, X. Fang, and J. Tang. 2016. Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones. IEEE/ACM Transactions on Networking 24, 3 (2016), 1732–1744.

Digital Library

[41]

Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 2(2019), 1–19.

Digital Library

[42]

Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays. 2018. Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv:1812.02903(2018).

[43]

Hao Yu, Sen Yang, and Shenghuo Zhu. 2019. Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5693–5700.

Digital Library

[44]

Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni. 2019. Bayesian nonparametric federated learning of neural networks. arXiv preprint arXiv:1905.12022(2019).

[45]

Rongfei Zeng, Shixun Zhang, Jiaqi Wang, and Xiaowen Chu. 2020. FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC. arXiv preprint arXiv:2002.09699(2020).

[46]

Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582(2018).

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cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
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Published: 03 June 2021

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https://doi.org/10.3390/s24102983
Hong YZheng ZWang Z(2024)A Multi-Dimensional Reverse Auction Mechanism for Volatile Federated Learning in the Mobile Edge Computing SystemsElectronics10.3390/electronics1316315413:16(3154)Online publication date: 9-Aug-2024
https://doi.org/10.3390/electronics13163154
Cai HBian CSheng BZhou JLi JHe X(2024)QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated LearningApplied Sciences10.3390/app1402083314:2(833)Online publication date: 18-Jan-2024
https://doi.org/10.3390/app14020833
张雨(2024)Analysis of Optimal Delegation Strategies for Federated Learning in the Context of Online Trade TransactionsE-Commerce Letters10.12677/ecl.2024.13225913:02(2130-2141)Online publication date: 2024
https://doi.org/10.12677/ecl.2024.132259
Wan TJiang TLiao WJiang N(2024)Hierarchical Incentive Mechanism for Federated Learning: A Single Contract to Dual Contract Approach for Smart IndustriesInternational Journal of Intelligent Systems10.1155/2024/64020262024(1-15)Online publication date: 4-Jan-2024
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