Abstract
The rapid growth of Internet of Things (IoT)-based healthcare systems has raised significant concerns regarding data privacy and security. Ensuring privacy while maintaining the utility of healthcare data remains a major challenge in IoT-enabled healthcare environments. This article proposes a novel data anonymization framework based on federated learning and adaptive differential privacy. Initially, IoT healthcare data are processed using a residual bidirectional gated recurrent unit (Res-BiGRU) model to capture contextual and privacy-related features. Subsequently, an adaptive differential privacy mechanism is applied to minimize information loss while ensuring strong privacy protection. To improve optimization performance, a modified Resilient Adaptive Apiary Organizational Optimization Algorithm (RAOOA) incorporating a fitness-based adaptive factor is introduced to enhance convergence stability and solution quality. The effectiveness of the proposed framework is evaluated using a healthcare dataset. The proposed approach demonstrates superior performance compared with existing methods in terms of computation time, information loss, and privacy risk. The results indicate that the proposed framework provides an efficient and scalable solution for privacy-preserving IoT healthcare data management while achieving an improved balance between data utility, computational efficiency, and privacy protection.