Belt and Braces: When Federated Learning Meets Differential Privacy
Abstract
Federated learning (FL) has great potential for large-scale machine learning (ML) without exposing raw data. Differential privacy (DP) is the de facto standard of privacy protection with provable guarantees. Advances in ML suggest that DP would be a perfect fit for FL with comprehensive privacy preservation. Hence, extensive efforts have been devoted to achieving practically usable FL with DP, which however is still challenging. Practitioners often not only are not fully aware of its development and categorization, but also face a hard choice between privacy and utility. Therefore, it calls for a holistic review of current advances and an investigation into the challenges and opportunities for highly usable FL systems with a DP guarantee. In this article, we first introduce the primary concepts of FL and DP, and highlight the benefits of integration. We then review the current developments by categorizing different paradigms and notions. Aiming at usable FL with DP, we present the optimization principles to seek a better tradeoff between model utility and privacy loss. Finally, we discuss future challenges in the emergent areas and relevant research topics. Building federated learning with differential privacy to train and refine machine-learning models with more comprehensive datasets can help exploit the potential of machine learning to its fullest.
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