Federated Learning: A Sustainable and Privacy-Preserving Approach for Medical AI Applications

Abstract: Artificial Intelligence (AI) has revolutionized healthcare, offering advanced solutions for diagnostics, treatment, and patient care. However, centralized AI systems face significant challenges, including data privacy concerns, high energy consumption, and a substantial carbon footprint. Federated learning (FL) presents a promising alternative, enabling collaborative model training while ensuring data privacy and reducing environmental impact. This paper explores the role of FL in addressing these challenges, its potential applications in healthcare, and future directions for sustainable and secure AI development.

Keywords: Federated learning, Deep Learning, Artificial Intelligence, Secure aggregation, Differential privacy