Application Domains of Federated Learning in Healthcare 5.0

Abstract

Federated learning has emerged as a game-changing approach in machine learning, allowing high-quality centralised models to be trained across a network of decentralised clients. Federated Learning is defined by the collaborative learning process that involves a large number of customers, each of whom contributes insights from their localised datasets. This collaborative approach is critical in cases where data privacy and network constraints are critical. This research focuses on the unique learning algorithms built for this situation. Individual clients autonomously compute model changes based on their local data at each iteration, then communicate these modifications to a central server. These client-side updates are subsequently aggregated by the central server, resulting in the construction of an updated global model. The challenge in this situation is to train models efficiently while dealing with clients who have inconsistent and slow network connections.