Adaptive Federated Multi Agent Deep Reinforcement Learning for Clinical Imaging Applications

Abstract

This paper introduces FedMediX, an adaptive federated multi agent deep reinforcement learning (MARL) framework for radiological data analysis. FedMediX accurately predicts multiple diseases from different types of diagnostic scans by involving a collaborative deep learning model trained across hospitals without sharing patient data. It is also able to automatically identify the type of medical image and explain the prediction using heatmaps. To address privacy constraints among multiple hospitals and non-stationary growth of medical data, federated learning is incorporated with a multi-agent reinforcement learning controller. To handle the non-IID data and enhance robustness, the federated learning involves a hybrid aggregation strategy incorporating attention mechanisms, trust scoring, and robust filtering. With a shared backbone, it automatically classifies the imaging modalities before disease-specific inference. This framework enables dynamic client selection and includes a Grad-CAM-based explainable AI providing visual justification for model predictions. Evaluation results on datasets confirm improved accuracy, faster convergence, and enhanced robustness of the proposed system.

Ähnliche Arbeiten