Momentum Gradient Descent Federated Learning with Local Differential Privacy
This addresses privacy concerns in federated learning for distributed data analysis, but it appears incremental as it combines existing techniques.
The authors tackled the challenge of preserving privacy in distributed learning by integrating federated learning with local differential privacy and momentum gradient descent to improve model performance, though no concrete numbers are provided.
Nowadays, the development of information technology is growing rapidly. In the big data era, the privacy of personal information has been more pronounced. The major challenge is to find a way to guarantee that sensitive personal information is not disclosed while data is published and analyzed. Centralized differential privacy is established on the assumption of a trusted third-party data curator. However, this assumption is not always true in reality. As a new privacy preservation model, local differential privacy has relatively strong privacy guarantees. Although federated learning has relatively been a privacy-preserving approach for distributed learning, it still introduces various privacy concerns. To avoid privacy threats and reduce communication costs, in this article, we propose integrating federated learning and local differential privacy with momentum gradient descent to improve the performance of machine learning models.