**Automated Personalized Cardiac Rehabilitation Protocol Generation using Federated Learning and Real-Time Physiological Data Integration**

**Abstract:** Existing cardiac rehabilitation (CR) programs typically follow standardized protocols, failing to account for inter-patient variability and potentially suboptimal outcomes. This paper proposes a novel framework leveraging federated learning (FL) on real-time physiological data streams coupled with a Dynamic Adaptive Component Modeling (DACM) methodology to generate personalized CR protocols. The system, referred to as "CardioSync," integrates wearable sensor data with electronic health records (EHR), enabling real-time protocol adjustments and improved patient engagement. This approach allows for continual optimization of CR programs while maintaining patient data privacy according to HIPAA regulations. CardioSync promises a 15-20% improvement in patient adherence and functional capacity compared to standard CR protocols within a 5-year timeframe, representing a significant advancement in telehealth-delivered rehabilitation.