A Systematic Survey for Differential Privacy Techniques in Federated Learning

Abstract

Federated learning is a distributed machine learning technique that trains a global model by exchanging model parameters or intermediate results among multiple data sources. Although federated learning achieves physical isolation of data, the local data of federated learning clients are still at risk of leakage under the attack of malicious individuals. For this reason, combining data protection techniques (e.g., differential privacy techniques) with federated learning is a sure way to further improve the data security of federated learning models. In this survey, we review recent advances in the research of differentially-private federated learning models. First, we introduce the workflow of federated learning and the theoretical basis of differential privacy. Then, we review three differentially-private federated learning paradigms: central differential privacy, local differential privacy, and distributed differential privacy. After this, we review the algorithmic optimization and communication cost optimization of federated learning models with differential privacy. Finally, we review the applications of federated learning models with differential privacy in various domains. By systematically summarizing the existing research, we propose future research opportunities.