research-article

A Fairness-aware Incentive Scheme for Federated Learning

Authors:

Qiang YangAuthors Info & Claims

AIES '20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Pages 393 - 399

https://doi.org/10.1145/3375627.3375840

Published: 07 February 2020 Publication History

Abstract

In federated learning (FL), data owners "share" their local data in a privacy preserving manner in order to build a federated model, which in turn, can be used to generate revenues for the participants. However, in FL involving business participants, they might incur significant costs if several competitors join the same federation. Furthermore, the training and commercialization of the models will take time, resulting in delays before the federation accumulates enough budget to pay back the participants. The issues of costs and temporary mismatch between contributions and rewards have not been addressed by existing payoff-sharing schemes. In this paper, we propose the Federated Learning Incentivizer (FLI) payoff-sharing scheme. The scheme dynamically divides a given budget in a context-aware manner among data owners in a federation by jointly maximizing the collective utility while minimizing the inequality among the data owners, in terms of the payoff gained by them and the waiting time for receiving payoff. Extensive experimental comparisons with five state-of-the-art payoff-sharing schemes show that FLI is the most attractive to high quality data owners and achieves the highest expected revenue for a data federation.

References

[1]

1978. The Belmont Report. Technical Report. National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, Department of Health, Education and Welfare, Washington, DC: United States Government Printing Office.

[2]

2016. REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Technical Report. European Union.

[3]

John Augustine, Ning Chen, Edith Elkind, Angelo Fanelli, Nick Gravin, and Dmitry Shiryaev. 2015. Dynamics of Profit-Sharing Games. Internet Mathematics 1 (2015), 1--22.

[4]

Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander. 2019. Towards Federated Learning at Scale: System Design. In CoRR. arXiv:1902.01046.

[5]

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 CCS. 1175--1191.

[6]

George Christodoulou, Kurt Mehlhorn, and Evangelia Pyrga. 2011. Improving the Price of Anarchy for Selfish Routing via Coordination Mechanisms. In ESA. 119--130.

[7]

Anhai Doan, Raghu Ramakrishnan, and Alon Y. Halevy. 2011. Crowdsourcing Systems on the World-Wide Web. Commun. ACM 54, 4 (2011), 86--96.

Digital Library

[8]

Boi Faltings, Goran Radanovic, and Ronald Brachman. 2017. Game theory for data science: Eliciting truthful information. Morgan & Claypool Publishers. 152 pages.

[9]

Dashan Gao, Yang Liu, Anbu Huang, Ce Ju, Han Yu, and Qiang Yang. 2019. Privacy-preserving heterogeneous federated transfer learning. In IEEE BigData.

[10]

Sreenivas Gollapudi, Kostas Kollias, Debmalya Panigrahi, and Venetia Pliatsika. 2017. Profit Sharing and Efficiency in Utility Games. In ESA. 1--16.

[11]

Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve G¨urel, Bo Li, Ce Zhang, Dawn Song, and Costas J. Spanos. 2019. Towards Efficient Data Valuation Based on the Shapley Value. In PMLR. 1167--1176.

[12]

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aur ´elien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G.L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, AdriGasc, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecn´y, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancre Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer ¨Ozg¨ur, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tram

[13]

er, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao. 2019. Advances and Open Problems in Federated Learning. In CoRR. arXiv:1912.04977.

[14]

Konstantinos Kollias and Tim Roughgarden. 2015. Restoring Pure Equilibria to Weighted Congestion Games. ACM Transactions on Economics and Computation 3, 4 (2015), 21:1--21:24.

Digital Library

[15]

Tie Luo, Salil S. Kanhere, Jianwei Huang, Sajal K. Das, and Fan Wu. 2017. Sustainable Incentives for Mobile Crowdsensing: Auctions, Lotteries, and Trust and Reputation Systems. IEEE Communications Magazine 55, 3 (2017), 68--74.

Digital Library

[16]

Jason R. Marden and Adam Wierman. 2013. Distributed Welfare Games. Operations Research 61, 1 (2013), 155--168.

Digital Library

[17]

H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Aguera y Arcas. 2016. Federated Learning of Deep Networks using Model Averaging. In CoRR. arXiv:1602.05629.

[18]

Debasis Mishra and Dharmaraj Veeramani. 2007. Vickrey-Dutch procurement auction for multiple items. European Journal of Operational Research 180, 2 (2007), 617--629.

[19]

Michael J. Neely. 2010. Stochastic Network Optimization with Application to Communication and Queueing Systems. Morgan & Claypool Publishers. 211 pages.

Digital Library

[20]

Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai. 2017. Privacy-Preserving Deep Learning via Additively Homomorphic Encryption. IEEE Transactions on Information Forensics and Security 13, 5 (2017), 1333--1345.

[21]

Reza Shokri and Vitaly Shmatikov. 2015. Privacy-Preserving Deep Learning. In CCS. 1310--1321.

[22]

Philipp von Falkenhausen and Tobias Harks. 2011. Optimal Cost Sharing Protocols for Scheduling Games. In EC. 285--294.

[23]

Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology 10, 2 (2019), 12:1--12:19.

Digital Library

[24]

Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu. 2019. Federated Learning. Morgan & Claypool Publishers. 189 pages.

[25]

Shuo Yang, Fan Wu, Shaojie Tang, Xiaofeng Gao, Bo Yang, and Guihai Chen. 2017. On Designing Data Quality-Aware Truth Estimation and Surplus Sharing Method for Mobile Crowdsensing. IEEE Journal on Selected Areas in Communications 35, 4 (2017), 832--847.

Digital Library

[26]

Han Yu, Yang Liu, Xiguang Wei, Chuyu Zheng, Tianjian Chen, Qiang Yang, and Xiong Peng. 2019. Fair and Explainable Dynamic Engagement of Crowd Workers. In IJCAI. 6575--6577.

[27]

Han Yu, Chunyan Miao, Yiqiang Chen, Simon Fauvel, Xiaoming Li, and Victor R. Lesser. 2017. Algorithmic management for improving collective productivity in crowdsourcing. Scientific Reports 7, 12541 (2017).

[28]

Han Yu, Chunyan Miao, Cyril Leung, Yiqiang Chen, Simon Fauvel, Victor R. Lesser, and Qiang Yang. 2016. Mitigating Herding in Hierarchical Crowdsourcing Networks. Scientific Reports 6, 4 (2016).

[29]

Han Yu, Chunyan Miao, Zhiqi Shen, Cyril Leung, Yiqiang Chen, and Qiang Yang. 2015. Efficient Task Sub-delegation for Crowdsourcing. In AAAI. 1305--1311.

[30]

Han Yu, Chunyan Miao, Yongqing Zheng, Lizhen Cui, Simon Fauvel, and Cyril Leung. 2019. Ethically Aligned Opportunistic Scheduling for Productive Laziness. In AIES. 45--51.

[31]

Han Yu, Zhiqi Shen, Chunyan Miao, Cyril Leung, Victor R. Lesser, and Qiang Yang. 2018. Building Ethics into Artificial Intelligence. In IJCAI. 5527--5533.

Cited By

Yao MQi STian ZLi QHan YLi HQi Y(2025)Quantifying Bytes: Understanding Practical Value of Data Assets in Federated LearningTsinghua Science and Technology10.26599/TST.2024.901003430:1(135-147)Online publication date: Feb-2025
https://doi.org/10.26599/TST.2024.9010034
Han JHan YJing XHuang GMa Y(2025)DegaFL: Decentralized Gradient Aggregation for Cross-Silo Federated LearningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.350158136:2(212-225)Online publication date: Feb-2025
https://doi.org/10.1109/TPDS.2024.3501581
Meng LQi ZWu LDu XLi ZCui LMeng X(2025)Improving Global Generalization and Local Personalization for Federated LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.341745236:1(76-87)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2024.3417452
Show More Cited By

Index Terms

A Fairness-aware Incentive Scheme for Federated Learning

Recommendations

Dynamic incentive-aware learning: robust pricing in contextual auctions
NIPS'19: Proceedings of the 33rd International Conference on Neural Information Processing Systems

Motivated by pricing in ad exchange markets, we consider the problem of robust learning of reserve prices against strategic buyers in repeated contextual second-price auctions. Buyers' valuations for an item depend on the context that describes the item. ...
Incentive mechanism design for Federated Learning with Stackelberg game perspective in the industrial scenario
Graphical abstract
This paper proposed an Incentive Mechanism Design method for Federated Learning (FL) based on the Stackelberg Game. Modified NSGA-II is applied to tackle the bi-level multi-objective programming problem and find the Nash equilibrium. ...
Highlights
- An incentive mechanism for Federated Learning is designed based on the Stackelberg game theory.
- A modified NSGA-II is applied to find the Nash equilibrium of the Stackelberg game.
- A real-life industrial case study is used to verify ...
Abstract
Federated Learning (FL) is a typical decentralized Machine Learning framework in which clients invest resources to train their local models without sharing their data and then transmit the model parameters to the server for parameter aggregation. ...
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
In many practical settings, the decision makers have to learn their best actions by experimenting with possible options and collecting feedback (data) over time. It is often assumed that the collected data can be trusted as they reflect the ground truth. ...
Motivated by pricing in ad exchange markets, we consider the problem of robust learning of reserve prices against strategic buyers in repeated contextual second-price auctions. Buyers’ valuations for an item depend on the context that describes the item. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

AIES '20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

February 2020

439 pages

ISBN:9781450371100

DOI:10.1145/3375627

General Chairs:
Annette Markham
Aarhus University | Loyola University
,
Julia Powles
University of Western Australia
,
Toby Walsh
TU Berlin | University of New South Wales | Data61
,
Anne L. Washington
New York University

Copyright © 2020 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 ACM 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

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

AIES '20

Sponsor:

SIGAI

AIES '20: AAAI/ACM Conference on AI, Ethics, and Society

February 7 - 9, 2020

NY, New York, USA

Acceptance Rates

Overall Acceptance Rate 61 of 162 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

152
Total Citations
View Citations
4,229
Total Downloads

Downloads (Last 12 months)471
Downloads (Last 6 weeks)28

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yao MQi STian ZLi QHan YLi HQi Y(2025)Quantifying Bytes: Understanding Practical Value of Data Assets in Federated LearningTsinghua Science and Technology10.26599/TST.2024.901003430:1(135-147)Online publication date: Feb-2025
https://doi.org/10.26599/TST.2024.9010034
Han JHan YJing XHuang GMa Y(2025)DegaFL: Decentralized Gradient Aggregation for Cross-Silo Federated LearningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.350158136:2(212-225)Online publication date: Feb-2025
https://doi.org/10.1109/TPDS.2024.3501581
Meng LQi ZWu LDu XLi ZCui LMeng X(2025)Improving Global Generalization and Local Personalization for Federated LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.341745236:1(76-87)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2024.3417452
Hu-Bolz JReed MZhang KLiu ZHu J(2025)Federated data acquisition market: Architecture and a mean-field based data pricing strategyHigh-Confidence Computing10.1016/j.hcc.2024.1002325:1(100232)Online publication date: Mar-2025
https://doi.org/10.1016/j.hcc.2024.100232
Zhang JLi YWu DZhao YPalaiahnakote S(2025)SFFL: Self-Aware Fairness Federated Learning Framework for Heterogeneous Data DistributionsExpert Systems with Applications10.1016/j.eswa.2025.126418(126418)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2025.126418
Zhu FHu FZhao YChen BTan X(2024)A Secure and Fair Federated Learning Framework Based on Consensus Incentive MechanismMathematics10.3390/math1219306812:19(3068)Online publication date: 30-Sep-2024
https://doi.org/10.3390/math12193068
Sheng XZhou YCui X(2024)Graph Neural Network Based Asynchronous Federated Learning for Digital Twin-Driven Distributed Multi-Agent Dynamical SystemsMathematics10.3390/math1216246912:16(2469)Online publication date: 9-Aug-2024
https://doi.org/10.3390/math12162469
Kim DWoo HLee Y(2024)Addressing Bias and Fairness Using Fair Federated Learning: A Synthetic ReviewElectronics10.3390/electronics1323466413:23(4664)Online publication date: 26-Nov-2024
https://doi.org/10.3390/electronics13234664
Yang XXiang SPeng CTan WWang YLiu HDing H(2024)Federated Learning Incentive Mechanism with Supervised Fuzzy Shapley ValueAxioms10.3390/axioms1304025413:4(254)Online publication date: 11-Apr-2024
https://doi.org/10.3390/axioms13040254
Kang YGu HTang XHe YZhang YHe JHan YFan LChen KYang Q(2024)Optimizing Privacy, Utility, and Efficiency in A Constrained Multi-Objective Federated Learning FrameworkACM Transactions on Intelligent Systems and Technology10.1145/3701039Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3701039
Show More Cited By

View Options

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten