A Cross-Domain Federated Learning Framework for Wireless Human Sensing

In this article, we study the problem of wireless human sensing, which refers to human activity recognition (HAR). HAR based on wireless signals plays an important role in security, human-computer interaction, and healthcare in the 5G era. Most state-of-the-art human activity recognition applications rely on deep learning approaches, which require a large amount of training data to achieve good performance. However, wireless signal data is difficult to collect and label, and it also carries private information, making it challenging to construct large-scale datasets.The recent advances in federated learning provide a chance to aggregate a wide range of users to collaboratively train a HAR model using decentralized datasets under data-preserving constraints. However, since a wireless signal is easily interrupted by the environment, the data across all participants is non-IID, thus decreasing the performance of an aggregated model. Additionally, due to the resource-constrained nature of edge devices, training the HAR model on an end user usually takes too long, resulting in straggler problems in federated learning training. In this article, we proposed a cross-domain federated learning framework (CDFL) to address the lack of labeled wireless data. A transfer learning approach was proposed to simulate wireless data by converting from widely available image datasets, and solving the distribution mismatch problem by domain adaption. Additionally, a customized federated learning approach was proposed to reduce the computational overhead of local model training. Using a case study of ultrasonic signal-based gesture recognition, we demonstrate the effectiveness of the proposed framework. Our method achieves over 90 percent accuracy on a 5-category task without real data, and 88 percent accuracy on a 10-category task when the user collects only one piece of data.