A Novel Meta-Federated Learning Framework with Distillation of Activation Boundaries for Human Activity Recognition in Manufacturing Industry

Human activity recognition (HAR) is necessary for detection of unsafe activity in industrial production, but there are still some issues that need to be solved, such as limited data in different scenarios and the lack of a unified model for different situations. Therefore, a novel meta-federated learning framework with distillation of activation boundaries (AB) is proposed, in which a federation is viewed as a meta-distribution and all federations work together without a central server. Specifically, the personalized model from the previous federation serves as the teacher model for the next federation, where general knowledge is extracted by AB knowledge distillation, the personalized knowledge is acquired through local training, and a high-quality model is obtained for the current federation by dynamically fusing general knowledge and personalized knowledge. To evaluate the effectiveness and superiority of the proposed framework, experiments were conducted on one popular HAR datasets (PAMAP2) and a chemical scenario dataset (WACID) constructed by our laboratory. The experimental results show that our proposed framework outperforms the state-of-the-art methods with fewer communication costs, achieving the recognition accuracies of 91.23% and 95.66% on the PAMAP2 dataset and WACID dataset, respectively.