Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
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Centralized solutions for privacy-preserving multi-party ML are becoming increasingly infeasible: a variety of attacks have demonstrated their weaknesses. Federated Learning is the current state of the art in supporting secure multi-party ML:data is maintained on the owner's device and is 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 maliciously use the byproducts of client data. As a response, we propose Biscotti: a fully decentralized P2P approach to multi-party ML, which leverages blockchain 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. Biscotti is able to protect the performance of the global model at scale even when 48 percent of the adversaries are malicious, and at the same time provides privacy while preventing poisoning attacks.
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