Personalized Human Activity Recognition Method Based on Federated Hierarchical Clustering Learning

Human activity recognition (HAR) plays a multi-dimensional supporting role in the medical field, providing strong technical support for various aspects such as disease prevention, diagnosis, treatment and rehabilitation. However, the use of traditional federated learning to deploy HAR models on edge devices is not ideal because of the heterogeneity of hardware and data. To solve this problem, this paper introduces a personalized HAR method, which can remove the outlier nodes and cluster hierarchically. In this study, the cosine similarity of local model parameters is calculated, and the clustering of dynamic clients is realized. In the study, the normalized training loss evaluation mechanism is introduced to identify and eliminate outlier nodes, and the robustness of the system is enhanced. In the study, the collaborative training method is adopted to meet the personalized needs of users and improve the generality of the model. The proposed method achieves an average recognition accuracy of 92.94% and an F1 score of 91.28% on four public datasets, demonstrating that the method put forward in this paper can reduce the negative impact of data heterogeneity, improve the efficiency of convergence, and produce good recognition performance for the development of the Internet of Things.