Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain

Baturalp Buyukates, Chaoyang He, Shanshan Han, Zhiyong Fang, Yupeng Zhang, Jieyi Long, Ali Farahanchi, Salman Avestimehr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider a project (model) owner that would like to train a model by utilizing the local private data and compute power of interested data owners, i.e., trainers. Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e.g., trainers aiming to poison the model); iv) verifiability in the sense that the integrity, i.e., correctness, of all computations in the data market protocol including contribution assessment and outlier detection are verifiable through zero-knowledge proofs; and v) efficient and universal design. We propose a blockchain-based marketplace design to achieve all five objectives mentioned above. In our design, we utilize a distributed storage infrastructure and an aggregator aside from the project owner and the trainers. The aggregator is a processing node that performs certain computations, including assessing trainer contributions, removing outliers, and updating hyper-parameters. We execute the proposed data market through a blockchain smart contract. The deployed smart contract ensures that the project owner cannot evade payment, and honest trainers are rewarded based on their contributions at the end of training. Finally, we implement the building blocks of the proposed data market and demonstrate their applicability in practical scenarios through extensive experiments.
Original languageEnglish
Title of host publication2023 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS)
PublisherIEEE
Pages13-22
Number of pages10
ISBN (Electronic)9798350335354
ISBN (Print)9798350335361
DOIs
Publication statusPublished - 6 Sept 2023
Externally publishedYes
Event2023 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS) - Athens, Greece
Duration: 17 Jul 202320 Jul 2023

Publication series

NameIEEE International Conference on Decentralized Applications and Infrastructures
PublisherIEEE
ISSN (Print)2835-348X
ISSN (Electronic)2835-3498

Conference

Conference2023 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS)
Country/TerritoryGreece
CityAthens
Period17/07/2320/07/23

Keywords

  • Training
  • Toxicology
  • Computational modeling
  • Smart contracts
  • Collaboration
  • Decentralized applications
  • Data models

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