Secure Collaborative Intelligence For Healthcare: Differential Privacy, Byzantine Robustness, and Temporal Deep Learning For Federated Patient Deterioration Prediction

We present the first comprehensive federated deep learning framework for real-time patient deterioration prediction that enables privacy-preserving collaboration across multiple healthcare institutions. Our framework integrates LSTM-based temporal modeling, streaming differential privacy, attention mechanisms for interpretability, and Byzantine-robust aggregation. Through extensive experiments on 1,000 patients across 5 heterogeneous hospitals, we demonstrate that federated temporal models achieve 77.6% accuracy (4.5% gap from centralized learning at 82.1%). The system provides clinically actionable early warnings 4-6 hours before deterioration with 79% sensitivity, maintains strong privacy at ε≈3, achieves 93% robustness under Byzantine attacks, and delivers attention-based explanations identifying critical time windows. Our results establish that real-time collaborative early warning systems are feasible with formal privacy guarantees and robust performance under realistic adversarial conditions.