Secure, accurate and privacy-aware fully decentralized learning via co-utility
Jesús Manjón, Josep Domingo‐Ferrer, David Sánchez, Alberto Blanco-Justicia · Computer Communications · 2023
Fully decentralized learning is a setting in which each peer in a P2P network trains a machine learning model with the help of the other peers. Each peer acts as a model manager by periodically sending her current model to other peers, who answer by returning model updates they compute on their private data. This creates a tension among privacy, accuracy and security. The privacy risk is that model updates returned by a peer can leak some of the peer’s private data. Unfortunately, distorting model updates to protect privacy works against the accuracy of the trained model. On the other hand, aggregating the updates of several peers and then sending the aggregate to the model manager may preserve privacy but it goes against security, because the model manager cannot filter out individual bad updates. Also, peers are autonomous and hence it cannot be taken for granted that they will honestly supply model updates to help the model manager train her model. To reconcile accuracy, privacy and security, we present a fully decentralized learning protocol such that: (i) it allows perfectly accurate individual updates to be returned by peers to the model manager in a privacy-preserving manner; (ii) it is co-utile by design, that is, it incentivizes rational peers to follow the protocol without deviating. The latter feature discourages rational attacks that might compromise security and it also deters free riding, thereby ensuring the sustainability of the protocol.