FedSDMU: A new Paradigm for Healthcare Data Privacy Compliance using Federated Synthesis, Differential Privacy, and Machine Unlearning

This study introduces FedSDMU, an innovative framework integrating Federated Learning (FL), Synthetic Data Generation (SDG), Differential Privacy (DP), and Machine Unlearning (MU) to enhance privacy-preserving healthcare analytics. FedSDMU enables collaborative model training across decentralized datasets without sharing raw data, safeguarding patient privacy. It strengthens security by injecting noise during training, preventing individual data inference. A key innovation is its federated synthesis, leveraging DP for secure generative model training and MU for data deletion, allowing patients to revoke consent while ensuring regulatory compliance. This framework outperforms existing models, strictly aligning with HIPAA and GDPR, while mitigating risks, enhancing prediction quality, addressing bias, and refining models by securely deleting data points. By tackling data scarcity, imbalance, and heterogeneity, FedSDMU establishes a robust and reliable analytical system. Its contributions pave the way for future studies, extending the use of privacy-preserving AI applications in healthcare.