Correlation-Aware Multi-Similarity Learning for Federated Human Activity Recognition

Centralized training for Human Activity Recognition (HAR) typically relies heavily on vast amounts of aggregated data, compromising user privacy. Federated learning (FL) for HAR offers a solution to protect local data privacy. However, existing FL methodologies often fail to fully capture the heterogeneity of user data and the latent correlations among user models, resulting in suboptimal performance and limited robustness. This paper proposes a Correlation-Aware Multi-Similarty Learning Method for Federated HAR, namely MultiSim. Our approach enhances model accuracy with an effective inter-user knowledge learning while protecting data privacy. MultiSim first constructs multiple similarity metrics, and then makes model feature fusion cunningly by the above metrics to learn inherent user similarity profiles. Additionally, we introduce a novel clustering-based FL framework by isolating malicious nodes, thereby mitigating the impact of adversarial attacks. Extensive evaluations on two realworld HAR datasets demonstrate the superiority of MultiSim over other state-of-the-art FL methods under accuracy and robustness. These findings demonstrate MultiSim's potential as a robust and effective solution for HAR.