Federated Deep Learning Framework for Privacy-Preserving Medical Image Classification

Abstract

The rapid increase in the use of deep learning, despite improving diagnostic accuracy in medical image analysis, has also raised significant concerns regarding regulatory compliance, data protection, and ensuring the privacy of the distribution of patient information. Traditional centralized learning methods are needed combining sensitive medical photographs from various healthcare facilities integrate institutions into a single repository, increasing the possibility that data violations and breaches of data protection legislation such as the GDPR as well as HIPAA. This research proposes a federated deep learning method. A system for categorizing medical images while ensuring privacy, enabling joint model training in decentralized medical centers while keeping actual patient information confidential. The framework employs federated learning to train convolutional neural networks models by iteratively adding updates that have been trained locally, maintain a high classification while guaranteeing the secrecy of the data performance. The tests reveal that the suggested method achieves competitive accuracy and precision, recovery and F1 score compared to centralized models, while significantly increasing regulatory compliance, security, and privacy. The suggested system provides a pragmatic and adaptable method for secure and decentralized evaluation of medical images in real healthcare environments.