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Title: Blockchain-enabled federated learning with mechanism design
Authors: Toyoda, Kentaroh
Zhao, Jun
Zhang, Allan Neng Sheng
Mathiopoulos, Panagiotis Takis
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Toyoda, K., Zhao, J., Zhang, A. N. S., & Mathiopoulos, P. T. (2020). Blockchain-enabled federated learning with mechanism design. IEEE Access, 8, 219744-219756. doi:10.1109/access.2020.3043037
Project: P20-O1-068USV
Journal: IEEE Access
Abstract: Federated learning (FL) is a promising decentralized deep learning technique that allows users to collaboratively update models without sharing their own data. However, due to its decentralized nature, no one can monitor workers’ behavior, and they may thus deviate protocols (e.g., participating without updating any models). To solve this problem, many researchers have proposed blockchain-enabled FL to reward workers (or users) with cryptocurrencies to encourage workers to follow the protocols. However, there is a lack of theoretical discussions concerning how such rewards impact workers’ behavior and how much should be given to workers. In this paper, we propose a mechanism-design-oriented FL protocol on a public blockchain network. Mechanism design (MD) is often used to make a rule intended to achieve a specific goal. With MD in mind, we introduce the concept of competition into blockchain-based FL so that only workers who have contributed well can obtain rewards, which naturally prevents workers from deviating from the protocol. We then mathematically answer the following questions with contest theory, a novel field of study in economics: i) What behavior will workers take?; ii) how much effort should workers exert to maximize their profits?; iii) how many workers should be rewarded?; and iv) what is the best proportion for reward distribution?.
ISSN: 2169-3536
DOI: 10.1109/access.2020.3043037
Schools: School of Computer Science and Engineering 
Rights: © 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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