Adverse drug reactions are often under-detected when analyses are limited to single institutions, and centralized pooling of electronic health records is frequently constrained by privacy regulations, institutional governance, and technical barriers to data transfer. Current multi-institutional pharmacovigilance typically relies on aggregate summaries rather than collaborative machine learning over distributed individual-level records, leaving complex temporal, clinical, and medication-related patterns difficult to detect across health systems. To address these challenges, this article proposes a federated learning framework that enables hospitals to jointly train adverse drug reaction prediction models using their own electronic health records, keeping patient-level data within each institution while exchanging only model updates through a controlled workflow. The framework incorporates a local model trainer at each hospital, a secure aggregation server, a differential privacy module, and a global model distribution layer, supporting structured electronic health record data, clinical text features, and interoperable pharmacovigilance definitions. By facilitating learning from more diverse clinical populations while preserving institutional data sovereignty, this federated approach could enhance adverse drug reaction prediction, promote earlier safety signal detection, and enable more reliable risk–benefit assessments in routine care, contingent on careful attention to privacy safeguards, data harmonization, governance, and workflow integration.