Harnessing Federated Learning for Disease Classification: A Paradigm Shift in Medical Diagnostics

Abstract

In the rapidly evolving landscape of healthcare technology, federated learning (FL) emerges as a pivotal innovation, particularly in the domain of disease classification. This paradigm shift, grounded in the principles of decentralized machine learning, promises to navigate the complex interplay between data privacy and the insatiable demand for more accurate and personalized medical diagnostics. By enabling collaborative model training across distributed data sources without compromising the privacy of sensitive medical data, federated learning stands at the forefront of a new era in medical diagnostics. This chapter delves into the intricacies of implementing federated learning for disease classification, exploring the challenges, methodologies, and potential it harbors for transforming health- care outcomes. Through a comprehensive analysis, we elucidate the steps from data preparation to model deployment, highlight the overcoming of pertinent challenges, and forecast emerging trends that will shape the future of federated learning in healthcare. This discourse not only showcases the advantages of fed- erated learning over traditional centralized approaches but also underscores the