System and method for privacy-preserving distributed training of machine learning models on distributed datasets

Abstract

A system for privacy-preserving distributed training of a global model on distributed datasets has a plurality of data providers being communicatively coupled. Each data provider has a local model and a local training dataset for training the local model using an iterative training algorithm. Further it has a portion of a cryptographic distributed secret key and a corresponding collective cryptographic public key of a multiparty fully homomorphic encryption scheme. All models are encrypted with the collective public key. Each data provider trains its local model using the respective local training dataset, and combines the local model with the current global model into a current local model. A data provider homomorphically combines current local models into a combined model, and updates the current global model based on the combined model. The updated global model is provided to at least a subset of the other data providers.