Federated Learning Frameworks for Multi-Hospital Drug Safety Prediction

Abstract

Drug safety prediction is a critical component of modern healthcare because adverse drug reactions (ADRs) remain a major cause of hospitalization, increased healthcare costs, and preventable mortality worldwide. Conventional machine learning approaches require centralized patient datasets, creating significant concerns regarding patient privacy, institutional regulations, and compliance with healthcare data protection laws. Federated Learning (FL) has emerged as an effective privacy-preserving artificial intelligence paradigm that enables multiple hospitals to collaboratively train predictive models without sharing sensitive patient information. This manuscript presents a comprehensive overview of federated learning frameworks for multihospital drug safety prediction by examining their architecture, learning mechanisms, security considerations, and clinical applications. The study reviews recent developments in distributed deep learning, privacy-preserving techniques, and healthcare AI while highlighting the advantages of collaborative intelligence over centralized learning. Existing literature demonstrates that federated models achieve prediction performance comparable to centralized models while substantially improving data privacy and institutional collaboration. The review identifies current challenges including data heterogeneity, communication overhead, model poisoning, and regulatory compliance, and discusses emerging research directions for secure, explainable, and scalable federated healthcare systems