Personalized Federated Attention State Space Learning for Wearable Human Activity Recognition

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Wearable human activity recognition (HAR) focuses on classifying human activities from multi-sensor data collected by wearable devices and has become increasingly important in pervasive computing. However, existing methods face several challenges: 1) aggregating heterogeneous local models while preserving user-specific data distributions, 2) achieving personalized adaptation of global models to diverse behavioral patterns, and 3) capturing both local and global temporal dependencies inherent in sensor time-series data. To address these challenges, we propose PFLMamba, a personalized federated learning framework integrating an attention-enhanced state-space model (ASSM) for hierarchical temporal feature extraction. PFLMamba employs a server-side personalized attention aggregation mechanism to tailor global models for individual clients, while ASSM captures both local and long-range temporal patterns on the client side. Extensive evaluations on the WISDM and PAMAP2 datasets demonstrate that PFLMamba achieves F 1 scores of 91.83 and 96.01, respectively, outperforming state-of-the-art federated learning baselines such as FCLFD, EFDLS, and FKD. PFLMamba’s effectiveness is further validated on multi-user and heterogeneous-sensor datasets, namely UCI-HAR and UNIMIB-SHAR, confirming its generalization across diverse populations and device types. Beyond predictive accuracy, PFLMamba exhibits a favorable trade-off between efficiency and performance, with lower client-side training overhead than teacher-student-based frameworks and competitive throughput relative to lightweight alternatives. Further experiments on resource-constrained edge devices (i.e., Raspberry Pi 4 and PYNQ-Z2) validate its practical feasibility, highlighting low-latency inference and moderate energy consumption. These results establish PFLMamba as a robust and efficient solution for personalized wearable HAR.