Personalized Privacy-Preserving Federated Learning: Optimized Trade-off Between Utility and Privacy

The emerging federated learning (FL) offers a feasible solution for the privacy preservation of users' sensitive data in training artificial intelligence (AI) models. Meanwhile, differential privacy (DP) is widely used in FL to ensure that data privacy is not disclosed during model training. However, in the practical deployment of DP in FL, a prominent challenge is that most existing FL solutions set the same privacy level for different users, resulting in over-protection for some users while insufficient protection for others. In this paper, we propose a novel federated learning framework with user-level personalized privacy protection (named FLUP) to meet the personalized privacy requirements of different users while maintaining high data utility. In this framework, we propose a user-level personalized DP mechanism that combines a personalized sampling algorithm and Gaussian perturbation to meet each user's personalized differential privacy corresponding to their privacy parameters. Then, we qualitatively analyze the impact of the sampling threshold on model performance. Furthermore, to balance user privacy requirements and AI model performance, we design a utility-aware game model to distributively determine the optimized sampling threshold and the users' differential privacy parameters. Finally, by conducting validation experiments, we demonstrate the feasibility and effectiveness of our proposed framework in terms of model performance as well as user privacy preservation.