AI-Based Federated Learning for Privacy-Preserving Healthcare AI Models

The rapid integration of artificial intelligence (AI) into medicine offers a future of early disease detection, personalized care, and predictive analytics that have never been seen before, yet the conventional centralized AI systems demand that sensitive patient data be aggregated which may result in critical privacy, ethical, and regulatory consequences. The legal limits on hospitals and clinics tend to hamper the sharing of raw health records as well as lead to the disintegration of the data set and hinders the development of precise and generalizable AI models. In addition, patient demographic, disease rates, and data type vary between organizations and can introduce heterogeneity further complicating the centralized application of AI. In order to address these drastic challenges, this research is providing a federated learning system (based on AI) which is specially customized to privacy protection AI solutions in healthcare. The architecture enables multiple medical centers to collectively train deep learning models without any information exchange (not even the raw patient data), using a secure aggregation scheme and adaptive differential privacy such that no confidential data is disclosed. Local models are scaled to the data of the particular institution, so the impact of non-identically distributed data becomes less, and high predictive accuracy is maintained. Simulation of different healthcare data (e.g. electronic health records and medical imaging data) was performed in many institutions with diverse patient populations and potential adversarial scenarios. The proposed plan showed good performance levels, and the accuracy and precision, as well as the recall and F1-score are higher than the traditional centralized AI or those that have not had privacy enhancements. These studies indicate that the framework does not only ensure confidentiality of the patient, but also makes precise and high quality predictions in a heterogeneous healthcare environment. This federated learning platform provides a viable, scalable and safe medium to implement AI within the health facilities that have more patients without infringing the strict privacy legislation.

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