A Comprehensive Survey on Federated Learning and Explainable AI for Gait-Based Activity Recognition Focusing on Techniques, Datasets, And Challenges

Gait-based activity recognition has gained significant attention in applications such as healthcare, security, and rehabilitation. However, traditional centralized machine learning models pose challenges related to data privacy, scalability, and interpretability. Federated Learning (FL) addresses these concerns by enabling distributed model training without sharing raw data, ensuring privacy preservation. Simultaneously, Explainable AI (XAI) techniques enhance model transparency, making gait recognition systems more interpretable. This paper presents a comprehensive survey on FL and XAI techniques for gait-based activity recognition, focusing on recent advancements, benchmark datasets, and existing challenges. A comparative study of different FL methods (FedAvg, FedProx, FedBN, Personalized FL, and Hierarchical FL) and XAI techniques (SHAP, LIME, Grad-CAM, Attention-based XAI, and Hybrid Neuro-Fuzzy models) demonstrates their effectiveness. Our findings show that Hierarchical FL combined with Hybrid XAI achieves the highest accuracy (93.1%) while maintaining strong privacy (0.91 score), albeit at a higher computational cost. Despite significant advancements, challenges such as communication efficiency, model personalization, and computational overhead persist. This study highlights the need for standardized benchmarks and optimized FL-XAI frameworks to enhance realworld deployment in resource-constrained environments.