Permissioned Blockchain Frame for Secure Federated Learning

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...

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Bibliographic Details
Published in:IEEE communications letters 2022-01, Vol.26 (1), p.13-17
Main Authors: Sun, Jin, Wu, Ying, Wang, Shangping, Fu, Yixue, Chang, Xiao
Format: Article
Language:English
Subjects:
Blockchain
Blockchains
Collaborative work
Cryptography
Data models
Distributed ledger
Encryption
Fabrics
Federated learning
homomorphic encryption
Machine learning
New technology
Peer-to-peer computing
Privacy
Security
security and privacy
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Summary: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.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2021.3121297