Abstract: Federated learning (FL), as a distributed machine learning paradigm, enables multiple users to train machine learning models locally using individual data and then update global model in a ...

Abstract: Bridging the advantages of differential privacy in both centralized model (i.e., high accuracy) and local model (i.e., minimum trust), the shuffle privacy model has potential applications in ...