Federated multimodal learning for privacy-preserving blood donor profiling and personalized recall strategy optimization

Blood supply shortages remain a persistent challenge for transfusion services, yet effective donor recall is hampered by fragmented data across institutions, limited profiling depth, and increasing privacy regulations. This paper proposes a federated multimodal learning framework that enables distributed blood centers to collaboratively train a shared donor profiling model without exchanging raw records. The framework integrates three modality-specific encoders - for demographic attributes, behavioral donation sequences, and textual feedback - unified through a cross-modal attention mechanism with adaptive gating. Differential privacy is embedded into the federated training pipeline via local gradient clipping and calibrated Gaussian noise injection, with cumulative privacy expenditure tracked through a moments accountant. Building on the fused donor embeddings, a multi-task recall optimization model jointly predicts optimal contact timing and communication channel, while a hierarchical clustering scheme translates predictions into tiered intervention protocols. Experiments on 127,463 donor records partitioned across six simulated blood centers demonstrate that the proposed method achieves a profiling F1 score of 83.7% and a recall conversion rate of 31.2%, approaching centralized performance within approximately 2% points. Ablation analysis confirms that the behavioral sequence modality contributes the strongest discriminative signal, while cross-modal attention yields a 4.3-point F1 improvement over naive concatenation. The privacy-utility tradeoff analysis identifies an operating range of ε ∈ [1.0, 4.0] that preserves over 93% of noise-free model utility, offering practitioners a principled basis for balancing data protection with operational effectiveness.