Authors: A. Priyadharsini
Abstract: The rapid adoption of artificial intelligence in the healthcare sector has led to an increased demand for high-quality medical datasets. However, the sensitive nature of patient information and the strict regulatory requirements surrounding healthcare data often restrict institutions from sharing data with external entities. Federated Medical Learning (FedMed) presents a promising solution by enabling multiple healthcare institutions to collaboratively train deep learning models without exposing raw patient data. This paper proposes a robust FedMed framework that integrates federated averaging, secure aggregation, and advanced privacy-preserving techniques to ensure confidentiality while maintaining high model performance. Experiments conducted using medical imaging datasets demonstrate that the FedMed model achieves accuracy levels comparable to centralized deep learning approaches, while significantly reducing privacy risks. The findings highlight the potential of FedMed to enable scalable, secure, and efficient AI-driven healthcare applications across diverse medical environments.
