Scalable Federated-Learning and Internet-of-Things enabled architecture for Chest Computer Tomography image classification

ABSTRACT

The recent proliferation of the Internet of Medical Things (IoMT), Federated Learning (FL), and Deep learning have opened new dimensions of research across the globe. This paper proposes the combined use of these paradigms to detect COVID-19 in Computer Tomography (CT) images. Initially, the framework collects the CT images at the various local hospital using IoMT and aggregated them in an Hadoop Distributed File system (HDFS) Spark big data framework for storage. Later, the proposed framework performs the model training in isolation with the trained parameters being sent to a centralized server for aggregation using federated Learning. The comprehensive experimentation is performed on three different COVID-19 databases to test the efficacy of the proposed work. The numerical investigation revealed that the proposed work outperforms existing techniques by a good margin. Also, the global server, when compared to the local server, achieves a 7.57% performance improvement in terms of accuracy and 3.33% in terms of Area Under Curve (AUC).