Harnessing Federated Learning for Efficient Analysis of Large-Scale Healthcare Image Datasets in IoT-Enabled Healthcare Systems - Padthe, Adithya; Ashtagi, Rashmi; Mohite, Sagar; Gaikwad, Prajakta; Bidwe, Ranjeet; Naveen, H. M. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Federated learning is a machine learning technique that allows multiple devices to collaboratively train a machine learning model without having to share their raw data. This is important for privacy-sensitive applications, such as healthcare, where the data cannot be shared with a central server. This paper proposes a federated learning framework for efficient analysis of large-scale healthcare image datasets in IoT-enabled healthcare systems. The framework uses a combination of federated averaging and transfer learning to train a machine learning model that can be deployed to multiple IoT devices. To evaluate the framework on a real-world healthcare dataset of chest X-ray (CXR) images and show that it can achieve state-of-the-art accuracy in classifying pneumonia while preserving the privacy of the data. The framework is designed to be scalable and efficient, so that it can be used to train machine learning models on large datasets of healthcare images. The results of experiments show that federated learning framework achieve state-of-the-art accuracy in classifying pneumonia on a real-world healthcare dataset of CXR images. Proposed work has the potential to revolutionize the way that healthcare image analysis is performed. By harnessing federated learning, it trains machine learning models on large datasets of healthcare images without having to share the raw data with a central server. This can help to protect the privacy of patients and improve the accuracy of healthcare diagnoses. Specifically, proposed federated learning framework achieved an accuracy of 98.87% in classifying pneumonia on the CXR images dataset. This is comparable to the accuracy of traditional machine learning models that are trained on the entire dataset. It believes that federated learning framework is a promising approach for healthcare image analysis. It is scalable, efficient, and privacy-preserving.