Federated Learning for Privacy-Preserving Medical Image Analysis: A Chest X-Ray Case Study

Abstract

A key component of contemporary healthcare, medical image analysis has transformed precision disease diagnosis, treatment planning, and disease monitoring. Advances in medical image processing, especially about the incorporation of deep learning techniques, have greatly improved the precision and efficacy of diagnostic processes. Federated learning protects patient anonymity by enabling cooperative model training across decentralized data sources, keeping sensitive medical data localized and private while still promoting model progress. This paper provides a complete medical image analysis framework using Federated Learning.  In a normalized and pre-processed Chest X-ray dataset, several base deep learning models were trained for comparison. These models included a basic CNN model, VGG16, ResNet50, and InceptionV3. After training, hyperparameters were optimized to improve performance. Our experimental results show that the Inception V3 performs better than other two DL models. The best-performing deep learning model was selected as the client’s local model. To address privacy concerns, Federated Learning (FL) techniques were employed. FL allows devices to update models locally without sharing raw data. The FedAvg algorithm was used at the server to aggregate the data received from the clients, and the process was performed cyclically. Model weights from individual devices were transmitted to a central server for aggregation. This collaborative approach enables learning while preserving data anonymity.