Federated Learning and Privacy-Preserving Drug Safety Analytics Section V
Abstract
Federated Learning (FL) offers a transformative approach to drug-safety analytics by enabling multi-institution collaboration without transferring sensitive patient data. As pharmacovigilance increasingly depends on multimodal evidence EHRs, genomics, imaging, biosignals, and real-world data traditional centralized models face challenges related to privacy, interoperability, and regulatory compliance. FL supports secure model training across hospitals, labs, and research networks, improving early toxicity detection, ADR prediction, and risk stratification while reducing bias across diverse populations. Enhanced with Privacy-Enhancing Technologies such as differential privacy, homomorphic encryption, and secure aggregation, FL aligns with global regulatory expectations for transparency and data protection. Case studies in oncology, immunology, and rare diseases show measurable gains in accuracy, sensitivity, and representation. FL thus establishes a scalable, ethical, and privacy-preserving foundation for next-generation pharmacovigilance and safe drug development.
