Personalized Federated Learning With Differential Privacy

TLDR

Machine learning on smart devices relies on large crowdsourced datasets, but sensitive user data pose privacy risks and the models must also capture individual characteristics for personalization. This article proposes a privacy‑preserving approach for learning effective personalized models on distributed user data while guaranteeing differential privacy. The method addresses user heterogeneity in a distributed setting and rigorously analyzes its convergence and differential‑privacy guarantees. Experiments on realistic mobile sensing data show the approach is robust to heterogeneity and achieves a favorable accuracy‑privacy trade‑off.

Abstract

To provide intelligent and personalized services on smart devices, machine learning techniques have been widely used to learn from data, identify patterns, and make automated decisions. Machine learning processes typically require a large amount of representative data that are often collected through crowdsourcing from end users. However, user data could be sensitive in nature, and training machine learning models on these data may expose sensitive information of users, violating their privacy. Moreover, to meet the increasing demand of personalized services, these learned models should capture their individual characteristics. This article proposes a privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data. Practical issues in a distributed learning system such as user heterogeneity are considered in the proposed approach. In addition, the convergence property and privacy guarantee of the proposed approach are rigorously analyzed. The experimental results on realistic mobile sensing data demonstrate that the proposed approach is robust to user heterogeneity and offers a good tradeoff between accuracy and privacy.