BTIMFL: A Blockchain-Based Trust Incentive Mechanism in Federated Learning

Minjung Park, Sangmi Chai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Federated learning (FL) is a machine learning technique that allows multiple devices to train a model collaboratively without sharing their data with a central server. It has advantages such as increased privacy, reduced communication costs, and improved scalability, making it useful in scenarios where data is distributed across multiple devices and privacy is a concern, such as in healthcare or finance. However, the potential for participants to behave selfishly in FL can be a challenge, and incentive mechanisms are needed to encourage them to participate in the training process. Incentives can take many strategies, such as financial rewards or reputation-based systems, and can be tailored to specific needs. In defense technology, FL can be used for predictive maintenance, target recognition, and intelligence analysis. However, the existing incentive mechanisms in FL have limitations, such as being complex to design and implement, and raising privacy concerns. To improve the incentive mechanisms in FL, we propose an incentive mechanism based on blockchain called BTIMFL that ensures transparency and effectiveness. The proposed mechanism includes DAO (Decentralized Autonomous Organizations) and smart contracts for the automatic distribution of profits to ensure fairness.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2023 Workshops, Proceedings
EditorsOsvaldo Gervasi, Beniamino Murgante, Francesco Scorza, Ana Maria A. C. Rocha, Chiara Garau, Yeliz Karaca, Carmelo M. Torre
PublisherSpringer Science and Business Media Deutschland GmbH
Pages175-185
Number of pages11
ISBN (Print)9783031371103
DOIs
StatePublished - 2023
Event23rd International Conference on Computational Science and Its Applications, ICCSA 2023 - Athens, Greece
Duration: 3 Jul 20236 Jul 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14106 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Computational Science and Its Applications, ICCSA 2023
Country/TerritoryGreece
CityAthens
Period3/07/236/07/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Blockchain
  • DAO (Decentralized Autonomous Organizations)
  • Federated Learning (FL)
  • Inventive Mechanism
  • Smart Contracts

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