{"title":"Research on Data Right Confirmation Mechanism of Federated Learning based on Blockchain","authors":"Xiaogang Cheng, Ren Guo","doi":"arxiv-2409.08476","DOIUrl":null,"url":null,"abstract":"Federated learning can solve the privacy protection problem in distributed\ndata mining and machine learning, and how to protect the ownership, use and\nincome rights of all parties involved in federated learning is an important\nissue. This paper proposes a federated learning data ownership confirmation\nmechanism based on blockchain and smart contract, which uses decentralized\nblockchain technology to save the contribution of each participant on the\nblockchain, and distributes the benefits of federated learning results through\nthe blockchain. In the local simulation environment of the blockchain, the\nrelevant smart contracts and data structures are simulated and implemented, and\nthe feasibility of the scheme is preliminarily demonstrated.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":"6 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Cryptography and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08476","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Federated learning can solve the privacy protection problem in distributed
data mining and machine learning, and how to protect the ownership, use and
income rights of all parties involved in federated learning is an important
issue. This paper proposes a federated learning data ownership confirmation
mechanism based on blockchain and smart contract, which uses decentralized
blockchain technology to save the contribution of each participant on the
blockchain, and distributes the benefits of federated learning results through
the blockchain. In the local simulation environment of the blockchain, the
relevant smart contracts and data structures are simulated and implemented, and
the feasibility of the scheme is preliminarily demonstrated.