Federated Deep Learning for Privacy-Preserving Healthcare Diagnostics in Smart Hospitals

Abstract

The rapid adoption of smart healthcare systems has enabled continuous patient monitoring through wearable sensors, Internet of Medical Things (IoMT) devices, and electronic health records. However, centralized machine learning approaches require sensitive medical data to be transferred to cloud servers, creating significant privacy and security concerns. This research proposes a federated deep learning framework that enables collaborative training across multiple hospitals without sharing patient data. The proposed architecture integrates Convolutional Neural Networks (CNNs) for medical image analysis and Long Short-Term Memory (LSTM) networks for physiological time-series prediction while employing secure aggregation and differential privacy mechanisms to protect patient information. Experimental evaluation demonstrates that the federated model achieves diagnostic accuracy comparable to centralized learning while significantly reducing privacy risks. Communication efficiency is enhanced through adaptive model compression techniques, making the framework suitable for resource-constrained healthcare environments. The proposed approach contributes to secure, scalable, and privacy-preserving intelligent healthcare systems capable of supporting next-generation clinical decision support.