Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
Abstract
Differentially private federated learning (DP-FL) offers a compelling approach to collaborative model training by ensuring robust privacy for clients. Despite its potential, current methods face challenges in effectively balancing privacy, utility, and performance across diverse federated learning scenarios. Addressing these challenges, we introduce UDP-FL, to our knowledge the first DP-FL framework that universally harmonizes any randomization mechanism, including those considered optimal, by employing the Gaussian Moments Accountant (viz. DP-SGD). Central to UDP-FL is the 'Harmonizer,' a dynamic module engineered to intelligently select and apply the most suitable DP mechanism tailored to each client's specific privacy requirements, data sensitivities, and computational capacities. This selection process is driven by the principle of Rényi Differential Privacy, which serves as a crucial mediator for aligning privacy budgets effectively. Our comprehensive evaluation of UDP-FL, benchmarked against established baseline methods, demonstrates superior performance in upholding privacy guarantees and enhancing model functionality. The framework's robustness has been rigorously tested against a broad spectrum of privacy attacks, making it one of the most thorough validations of a DP-FL framework to date.
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