Biscotti: A Blockchain System for Private and Secure Federated Learning

Federated Learning is the current state-of-the-art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. However, this process assumes a trusted centralized infrastructure for coord...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems 2021-07, Vol.32 (7), p.1513-1525
Main Authors: Shayan, Muhammad, Fung, Clement, Yoon, Chris J. M., Beschastnikh, Ivan
Format: Article
Language:English
Subjects:
Blockchain
Clients
Collaborative work
Cryptography
Data models
Distributed machine learning
Fault tolerance
Federated learning
Machine learning
Peer-to-peer computing
Privacy
security
Training
Training data
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Summary:Federated Learning is the current state-of-the-art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. However, this process assumes a trusted centralized infrastructure for coordination, and clients must trust that the central service does not use the byproducts of client data. In addition to this, a group of malicious clients could also harm the performance of the model by carrying out a poisoning attack. As a response, we propose Biscotti: a fully decentralized peer to peer (P2P) approach to multi-party ML, which uses blockchain and cryptographic primitives to coordinate a privacy-preserving ML process between peering clients. Our evaluation demonstrates that Biscotti is scalable, fault tolerant, and defends against known attacks. For example, Biscotti is able to both protect the privacy of an individual client's update and maintain the performance of the global model at scale when 30 percent adversaries are present in the system.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2020.3044223