Permissioned Blockchain Frame for Secure Federated Learning

【Author】 Sun, Jin; Wu, Ying; Wang, Shangping; Fu, Yixue; Chang, Xiao

【Source】IEEE COMMUNICATIONS LETTERS

【影响因子】3.553

【Abstract】Federated learning is an emerging technology that solves the privacy problem of training data in multi-party machine learning. However, this technology is vulnerable to a series of system security problems. In this letter, we leverage Hyperledger Fabric permissioned blockchain architecture to build a secure and reliable federated learning platform across multiple data owners, where individual local updates are encrypted based on threshold homomorphic encryption and then recorded on a distributed ledger. The security analysis shows that our solution can effectively deal with the existing privacy and security issues in the federated learning system. The numerical results show that the scheme is feasible and efficient.

【Keywords】Blockchains; Security; Fabrics; Collaborative work; Peer-to-peer computing; Data models; Privacy; Federated learning; blockchain; homomorphic encryption; security and privacy

【发表时间】2022 JAN

【收录时间】2022-02-02

【文献类型】期刊

【主题类别】

区块链技术--