Privacy-Preserving Federated Learning with Galois Automorphism-Driven Linear Transformation with Brakerski-Fan-Vercauteren for Medical Data

Healthcare data is frequently fragmented over diverse organizations because of its extremely complex and confidential nature. However, the existing Federated Learning (FL) approach through a central server creates various challenges within healthcare, such as privacy vulnerabilities and regulatory compliance. Thus, this research proposes the privacy-preserving FL approach with Fully Homomorphic Encryption (FHE) for protecting the patient’s sensitive data in medical records. In the proposed framework, the Galois Automorphism-driven Linear Transformation with Brakerski-Fan-Vercauteren, named GALT-BFV, is proposed for improving the medical data privacy and security. Moreover, this research introduces the pre-trained model of XceptionNet for training the local and global models in FL. Finally, the Federated Proximal (FedProx) approach is introduced for the aggregation of local and global models. The experimental discoveries establish that the proposed GALT-BFV method reaches better accuracies of 0.98 and 0.88 on Coronavirus Disease 2019 (COVID-19) X-ray and brain tumor Magnetic Resonance Imaging (MRI) datasets, compared to previous approaches.