FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring
arXiv:2603.06224v1 Announce Type: new Abstract: Wearable sensors with local data processing can detect health threats early, enhance documentation, and support 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). The proposed architecture is inspired by Party-Adaptive XGBoost (PAX) while explicitly preserving key structural and optimization properties of standard XGBoost, including histogram-based split construction and tree-ensemble dynamics. First, we provide a theoretical analysis showing that, under appropriate data conditions and suitable hyperparameter selection, the proposed distributed protocol can converge
arXiv:2603.06224v1 Announce Type: new Abstract: Wearable sensors with local data processing can detect health threats early, enhance documentation, and support 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). The proposed architecture is inspired by Party-Adaptive XGBoost (PAX) while explicitly preserving key structural and optimization properties of standard XGBoost, including histogram-based split construction and tree-ensemble dynamics. First, we provide a theoretical analysis showing that, under appropriate data conditions and suitable hyperparameter selection, the proposed distributed protocol can converge to solutions equivalent to centralized XGBoost training. Second, the protocol is empirically evaluated on a representative wearable-sensor HAR dataset, reflecting the heterogeneity and data fragmentation typical of remote monitoring scenarios. Benchmarking against centralized XGBoost and IBM PAX demonstrates that the theoretical convergence properties are reflected in practice. The results indicate that the proposed approach can match centralized performance up to a gap under 1\% while retaining the structural advantages of XGBoost in distributed wearable-based HAR settings.
Executive Summary
The article introduces FedSCS-XGB, a novel distributed machine learning protocol for human activity recognition from wearable sensor data. It is based on gradient-boosted decision trees and preserves key properties of standard XGBoost. The protocol is theoretically and empirically evaluated, demonstrating convergence to centralized XGBoost solutions and matching performance with a gap under 1%. This approach has potential for continual health monitoring, particularly for spinal cord injury patients.
Key Points
- ▸ FedSCS-XGB is a distributed machine learning protocol for human activity recognition
- ▸ It is based on gradient-boosted decision trees (XGBoost) and preserves key properties
- ▸ The protocol is evaluated theoretically and empirically, demonstrating convergence and performance
Merits
Preservation of XGBoost Properties
The proposed protocol preserves key structural and optimization properties of standard XGBoost, including histogram-based split construction and tree-ensemble dynamics.
Convergence to Centralized Solutions
The protocol can converge to solutions equivalent to centralized XGBoost training under appropriate data conditions and hyperparameter selection.
Demerits
Data Conditions and Hyperparameter Selection
The protocol's convergence and performance may be sensitive to data conditions and hyperparameter selection, which can be challenging in practice.
Expert Commentary
The proposed FedSCS-XGB protocol demonstrates significant potential for continual health monitoring, particularly in the context of spinal cord injuries. By preserving key properties of XGBoost and converging to centralized solutions, this approach can enable efficient and accurate human activity recognition from wearable sensor data. However, careful consideration of data conditions, hyperparameter selection, and data privacy and security regulations will be essential for practical deployment and widespread adoption.
Recommendations
- ✓ Further research is needed to evaluate the protocol's performance in diverse healthcare applications and to develop strategies for addressing data privacy and security concerns.
- ✓ Collaboration between researchers, clinicians, and policymakers will be essential for developing and deploying such protocols in practice, ensuring that they meet the needs of patients and healthcare providers while adhering to relevant regulations and standards.