Biscotti: A Ledger for Private and Secure Peer to Peer Machine Learning (under submission)


Centralized solutions for privacy-preserving multi-party ML are becoming increasingly infeasible. Google’s 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 to preserve privacy and is susceptible to poisoning attacks.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 while protecting the performance of the global model at scale even when 48% of the adversaries are malicious.

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