Federated Learning for Enhancing Reliability and Security in Medical Image Analysis Against Adversarial Threats

Abstract

Federated Learning (FL) has emerged as an attractive concept of facilitating privacy-saving artificial intelligence in the field of medicine. This paper suggests a secure and robust Federated Learning framework that enhances the robustness and privacy of medical image analysis, as well as safeguard the data confidentiality of decentralised healthcare organizations. The framework incorporates superior security protocols to protect the federated learning systems to model poisoning and adversarial interruptions. It enhances the resilience of the global model to inconsistent or malicious contributions of data by using adaptive aggregation, anomaly detection, and dynamic reputation-based client evaluation. It is more accurate, stable, and robust to use in the heterogeneous and non-IID environment, which guarantees the reliable cooperation of decentralized medical applications. The proposed structure comprises of a well-organised pipeline that involves data preprocessing, local training, and secure model aggregation. A convolutional neural network (CNN) is trained on histopathological images by each client and does not violate data privacy on an institutional level. The improved efficiency and reliability as well as the resilience of the model to disruptive, hostile training of histopathological images are experimentally demonstrated on a curated dataset of histopathological images. It can be proved that the proposed system is well-diagnostic with enhanced compliance with privacy and offers a flexible and dependable model of healthcare partnership on a large scale. This paper highlights how federated learning can advance improved clinical outcomes and enhance the quality of patient care by demonstrating the result of applying federated learning to multi-institutional image analysis and showing improvements in model security and performance.