DC-PFL: A dynamic clustering-based personalized federated learning method for human activity recognition

作者：Xiaoxu Wen, Yan Wang, Menghao Yuan, Aihui Wang, Zheng Ge, Hongnian Yu, Lin Meng · 发表于：Engineering Science and Technology an International Journal · 年份：2025 · DOI：10.1016/j.jestch.2025.102230 · 被引用次数：3 · 研究领域：Context-Aware Activity Recognition Systems、Privacy-Preserving Technologies in Data、Human Pose and Action Recognition

Human Activity Recognition (HAR) is essential in pervasive computing, healthcare, and human–computer interaction, where accurate interpretation of motion data underpins intelligent decision-making. Federated Learning (FL) enables privacy-preserving model training across distributed clients without sharing raw data, but suffers from degraded performance under Non-Independent and Identically Distributed (Non-IID) data, a common challenge in HAR due to user diversity and device heterogeneity. To address this, Personalized Federated Learning (PFL) introduces client-specific modeling, often via clustering. However, most existing approaches adopt static clustering strategies, lacking adaptability to dynamic changes in client data distributions. In this work, we propose DC-PFL, a Dynamic Clustering-based Personalized Federated Learning framework that performs round-wise client clustering using lightweight statistical features, like Average Peak Frequency (APF), percentiles, and Median Absolute Deviation (MAD) derived from local model parameters. This design ensures efficient and privacy-preserving similarity estimation across clients. By dynamically adjusting clusters during training, DC-PFL enables fine-grained personalization, better generalization, and improved robustness to Non-IID conditions. Experimental results on HAR benchmarks demonstrate that DC-PFL achieves superior performance in both accuracy and convergence speed compared to existing methods, including FedCHAR and stan...