Federated Learning for Enhanced Deep Learning Integration

Abstract

Federated Learning Enhanced Deep Learning Integration Abstract: Maintaining patient privacy while utilizing the combined power of dispersed datasets is critical in the field of healthcare. One interesting approach is federated learning (FL), which enables cooperation between different institutions without jeopardizing private medical data. This article investigates the use of improved deep learning models in four different health-related fields using the FL framework. Our goal is to improve predictive accuracy while preserving data privacy by combining FL techniques with cutting-edge deep learning frameworks. By using real-time data exchange across decentralized networks, we want to optimize model training by taking advantage of the latest developments in communication technology. The Enhanced FL Health Model (EFHM), as our proposed method is called, balances the advantages of various health datasets with the drawbacks of conventional centralized learning paradigms. We examine the consequences of incorporating domain-specific expertise to customize deep learning structures to the distinct features of every health domain. We provide insights into the potential future paths of federated learning in healthcare through a thorough examination of the benefits and difficulties associated with EFHM implementation.