Federated learning framework for Wi-Fi-based cross-environment human action recognition

Human action recognition (HAR) based on Wi-Fi plays a critical support in the Internet of Things (IoT). Recently, Wi-Fi-based HAR using deep learning models achieves remarkable performance. However, existing HAR models have poor generalization capacity, where the multipath effects and the recognition tasks diversity in different environments would affect the model performance at a great level. This article proposes a cross-environment HAR system based on the federated learning named WiFed-CHAR. This system collaboratively learns the action feature from source environments and generate a feature extraction knowledge base on the cloud. In addition, a HAR module assignment and optimization strategy is proposed to guide the new environment to inherit the most suitable feature extraction knowledge from knowledge base and achieve high performance even with limited data. Extensive experiments are conducted to validate the effectiveness of WiFed-CHAR. When given one sample/action, the HAR of new environments reaches 80.14%, surpassing other competitive baselines.