Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning

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 omit client contributions or use the byproducts of client data. As a response, we propose Biscotti: a fully decentralized P2P approach to multi-party ML, which uses blockchain and crypto 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 protect the performance of the global model at scale even when 45% of adversaries are trying to poison the model. The implementation can be found at: https://github.com/DistributedML/Biscotti