A Comparative Assessment of Adaptive Differential Privacy Preserving Mechanism for Secure Federated Mental Health Classification

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Mental health applications consist of sensitive and confidential information; maintaining privacy protection is a significant concern. Federated Learning (FL) is an effective privacy-preserving approach that helps to train models without exposing raw data. During processing of model updates, sensitive information may potentially be revealed; therefore, additional security protection measures are necessary. Differential Privacy (DP) is one of the privacy-preserving techniques that inject noise to the sensitive data, and it helps to reduce such risks, but there are still some disadvantages to the adaptive mechanisms. This paper thoroughly evaluated a combination of various adaptive methods that enabled DP with FL. It processes five combinations of adaptive methods, such as gradient-norm-based clipping with time-decay adaptive noise, percentile-based clipping with gradient-norm-based adaptive noise, Moving average adaptive clipping with round-based adaptive noise, layer-wise clipping with layer-wise adaptive noise, and median clipping with epoch-based noise scheduling. Using the DASS-21 mental health patient dataset, it assesses the privacy-utility trade-off through Accuracy, AUC, Precision, Recall, F1 score, Clipping behavior, and Privacy budget analysis. The experimental findings demonstrate that adaptive privacy strategies have a substantial impact on both model performance and privacy preservation; moving-average adaptive clipping and round-based adaptive noise combination achieve a more balanced result than others. These findings provide a strong foundation for developing advanced privacy-aware federated learning frameworks for highly delicate healthcare applications.