Personalized Longitudinal Medical Report Generation via Temporally-Aware Federated Adaptation

Automatic medical report generation from multimodal longitudinal imaging is crucial for clinical diagnosis but remains challenging due to privacy constraints and evolving disease dynamics. While federated learning (FL) enables decentralized model training without data sharing, its extension to longitudinal medical modeling remains underexplored. Existing FL approaches overlook temporal non-stationarity across visits and patient-specific heterogeneity, causing unstable optimization and degraded report quality.We introduce Federated Temporal Adaptation (FTA), a new FL setting for longitudinal medical report generation, and propose FedTAR, a framework combining parameter-efficient personalization and meta-learned temporal aggregation. FedTAR employs a metadata-conditioned LoRA module that generates patient-specific adapters from Gaussian-mixture embeddings and a residual temporal aggregation scheme that adaptively weights client updates via first-order MAML, ensuring stable and efficient optimization under temporal heterogeneity.Experiments on J-MID (1M exams) and MIMIC-CXR demonstrate consistent improvements in linguistic accuracy, temporal coherence, and cross-site generalization, establishing FedTAR as a robust, privacy-preserving paradigm for federated multimodal longitudinal modeling.