Biscotti: A Blockchain System for Private and Secure Federated Learning

By Muhammad Shayan, Clement Fung, Chris J. M. Yoon, Ivan Beschastnikh. IEEE Transactions on Parallel and Distributed Systems, 2021

329 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/93df42b5d2769a324a6c3bc03f82ae9227eee244

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In Artificial Intelligence

Citations in the corpus

Decentralized Federated Learning: A Survey and Perspective (2023, methodology): “Shayan et al. [229] proposed the Biscotti DFL system, which incorporates multiple privacy and security protection techniques, including the multi-Krum defense to prevent poisoning attacks, differential privacy noise to protect privacy, and secure aggregation.”