A Comparative Analysis of FedAvg, FedProx, and Scaffold in Gait-Based Activity Recognition by Evaluating Accuracy, Privacy, and Explainability

Gait-based activity recognition plays a crucial role in various applications, including healthcare monitoring, security surveillance, and biometric authentication. Traditional machine learning models for human activity recognition (HAR) rely on centralized data collection, raising concerns about privacy and data security. Federated Learning (FL) offers a promising solution by enabling distributed model training while preserving user data privacy. In this study, we conduct a comparative analysis of three FL algorithms—FedAvg, FedProx, and SCAFFOLD—evaluating their performance in terms of accuracy, privacy, and explainability. Using publicly available gait datasets (e.g., MobiAct, WISDM, OU-ISIR Gait), we simulate a federated environment where decentralized clients participate in model training. Our results indicate that SCAFFOLD achieves the highest accuracy (89.1%) and fastest convergence (70 rounds) due to variance reduction techniques, while FedProx improves stability across heterogeneous data. FedAvg, although less accurate, minimizes communication overhead (8.5 MB), making it suitable for resource-constrained devices. Furthermore, SCAFFOLD demonstrates superior privacy preservation (0.9 privacy score) and explainability (79.4), making it a strong candidate for interpretable and secure gait-based HAR applications. This study provides insights into selecting the most suitable FL algorithm based on the trade-offs between model performance, privacy, and interpretability