AI RESEARCH
FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring
arXiv CS.LG
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ArXi:2603.06224v1 Announce Type: new Wearable sensors with local data processing can detect health threats early, enhance documentation, and personalized therapy. In the context of spinal cord injury (SCI), which involves risks such as pressure injuries and blood pressure instability, continuous monitoring can help mitigate these by enabling early deDtection and intervention. In this work, we present a novel distributed machine learning (DML) protocol for human activity recognition (HAR) from wearable sensor data based on gradient-boosted decision trees (XGBoost.