Systems and Methods for Differentially Private Federated Machine Learning for Large Models and a Strong Adversary

Abstract

Systems and methods for federated learning are illustrated. A method for federated learning includes steps for identifying a first set of one or more devices as members of a master committee, identifying a second set of one or more devices as members of a differential privacy (DP)-noise committee, receiving a set of encrypted noise values for differential privacy from the members of the DP-noise committee, receiving, from a third set of one or more devices, a set of encrypted update values, and aggregating the encrypted noise values and the encrypted update values to produce encrypted aggregation results. The method further includes steps for receiving, from a fourth set of one or more devices, decrypted aggregation results based on cryptographic key shares of a private cryptographic key from the master committee, and updating model parameters of the model based on the decrypted aggregation results.

Dependencies are inferred from the claim text.