A Distributed Deep Learning Framework for Federated Big Medical Image Analysis

The use of big data in medical image analysis has time again proved to advanced the field by generating higher diagnostic accuracy and improved neural network performance. This has also led to the use of larger neural networks and increased security risk in sharing sensitive medical data information. In this paper, a pipeline is theorised to securely facilitate medical image analysis through reduced computational costs using federated learning and model compression frameworks.