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.

Inspired by the great success of using code coverage as guidance in software testing, a lot of neural network coverage criteria have been proposed to guide testing of neural network models (e.g., model accuracy under adversarial attacks). However, while the monotonic relation between code coverage and software quality has been supported by many seminal studies in software engineering, it remains largely unclear whether similar monotonicity exists between neural network model coverage and model quality. This paper sets out to answer this question. Specifically, this paper studies the correlation between DNN model quality and coverage criteria, effects of coverage guided adversarial example generation compared with gradient decent based methods, effectiveness of coverage based retraining compared with existing adversarial training, and the internal relationships among coverage criteria.