Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy
David R. Byrd, Vaikkunth Mugunthan, Antigoni Polychroniadou, Tucker Balch
Abstract
Federated learning enables a population of distributed clients to jointly train a shared machine learning model with the assistance of a central server. The finance community has shown interest in its potential to allow inter-firm and cross-silo collaborative models for problems of common interest (e.g. fraud detection), even when customer data use is heavily regulated. Prior works on federated learning have employed cryptographic techniques to keep individual client model parameters private even when the central server is not trusted. However, there is an important gap in the literature: efficient protection against attacks in which other parties collude to expose an honest client’s model parameters, and therefore potentially protected customer data.