research-article

A Hybrid Incentive Mechanism for Decentralized Federated Learning

Authors:

Yang XiangAuthors Info & Claims

Distributed Ledger Technologies: Research and Practice, Volume 1, Issue 1

Article No.: 4, Pages 1 - 15

https://doi.org/10.1145/3538226

Published: 09 September 2022 Publication History

Abstract

Federated Learning (FL) presents a privacy-compliant approach by sharing model parameters instead of raw data. However, how to motivate data owners to participate in and stay within an FL ecosystem by continuously contributing their data to the FL model remains a challenge. In this article, we propose a hybrid incentive mechanism based on blockchain to address the above challenge. The proposed mechanism comprises two primary smart contract-based modules, namely the reputation module and the reverse auction module. The former is used to dynamically calculate the reputation score of each FL participant. It employs a trust-jointed reputation scheme to balance the weights between trust values of parameters and bid prices. The latter is responsible for initiating FL auction tasks, calculating price rankings, and assigning corresponding token rewards. Experiments are conducted to evaluate the feasibility and performance of the proposed mechanism against the three typical threats. Experimental results indicate that our mechanism can successfully reduce incentive costs while preventing participants from colluding and over-bidding in the data sharing auction.

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Cited By

Martínez Beltrán EPérez MSánchez PBernal SBovet GPérez MPérez GCeldrán A(2023)Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and ChallengesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.331574625:4(2983-3013)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3315746
Park MChai S(2023)BTIMFL: A Blockchain-Based Trust Incentive Mechanism in Federated LearningComputational Science and Its Applications – ICCSA 2023 Workshops10.1007/978-3-031-37111-0_13(175-185)Online publication date: 3-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-37111-0_13

Index Terms

A Hybrid Incentive Mechanism for Decentralized Federated Learning
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Peer-to-peer architectures
  2. Dependable and fault-tolerant systems and networks

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Published In

cover image Distributed Ledger Technologies: Research and Practice

Distributed Ledger Technologies: Research and Practice Volume 1, Issue 1

September 2022

124 pages

EISSN:2769-6480

DOI:10.1145/3557023

Editors:
Kim-Kwang Raymond Choo
University of Texas San Antonio, United States
,
Mohammad Hammoudeh
Manchester Metropolitan University, United Kingdom

Issue’s Table of Contents

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].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2022

Online AM: 18 May 2022

Accepted: 01 April 2022

Revised: 01 March 2022

Received: 01 November 2021

Published in DLT Volume 1, Issue 1

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Cited By

Martínez Beltrán EPérez MSánchez PBernal SBovet GPérez MPérez GCeldrán A(2023)Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and ChallengesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.331574625:4(2983-3013)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3315746
Park MChai S(2023)BTIMFL: A Blockchain-Based Trust Incentive Mechanism in Federated LearningComputational Science and Its Applications – ICCSA 2023 Workshops10.1007/978-3-031-37111-0_13(175-185)Online publication date: 3-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-37111-0_13

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