ProLoRA: Resource-Efficient Personalized Federated Learning for Sensor Based Human Activity Recognition

The Internet of Things (IoT) has facilitated the generation of vast amounts of data, enabling advanced personalized healthcare services such as Human Activity Recognition (HAR) systems. Privacy concerns have driven the adoption of federated learning (FL) across multiple distributed healthcare devices. However, the lack of model adaptation for device-specific data, particularly in non-i.i.d. settings and the limited resource capabilities of these devices, presents an ongoing challenge for FL implementation on-devices. In this study, we introduce a new Personalized Orthogonal Low-Rank Adaptation (ProLoRA) method which provides efficient personalized HAR system. ProLoRA uses low-rank orthogonal transformations of the fully connected layers to mitigating interference with previously acquired personalized knowledge. Our method demonstrates superior personalized model performance and competitive global model accuracy while significantly reducing computational and memory overhead compared to existing state-of-the-art personalized FL techniques. Comprehensive empirical evaluations on HAR and PAMAP2 datasets validate the superior performance of ProLoRA in both accuracy and resource efficiency.