Abstract
Federated Learning (FL) is revolutionizing medical artificial intelligence (AI) by enabling collaborative model training across multiple healthcare institutions while ensuring patient data privacy. Unlike traditional centralized learning, FL allows hospitals and research centres to train AI models locally on their data, sharing only model updates instead of raw patient information. This approach enhances predictive analytics, medical imaging diagnostics, and personalized treatment recommendations while complying with stringent data protection regulations such as HIPAA and GDPR. Despite its advantages, FL faces challenges, including communication overhead, data heterogeneity, and security threats. This paper explores the potential of FL in medical applications, its implementation strategies, and emerging solutions to overcome its limitations. By advancing privacy-preserving AI, FL paves the way for secure, scalable, and collaborative healthcare innovations.