Co-sense

Abstract

Aiming at problems of under-fitting and poor model robustness in learning-based wireless sensing methods caused by the lack of large-scale wireless sensing datasets, this paper proposes a privacy-friendly collaborative wireless sensing framework, called Co-Sense. It builds a community with multiple clients and a server, which aggregates the clients' local models into a federated model with cross-domain capability. To protect the privacy of users' local data, we innovatively introduce the idea of federated learning into the field of wireless sensing, by uploading users' local model parameters instead of their local data. Then, in response to the uneven computing power of different users' edge devices, we propose a local model update algorithm based on adaptive computing power. Furthermore, a client selection algorithm based on test nodes is designed to reduce the negative influence of malicious clients on Co-Sense. Finally, we evaluate Co-Sense on three well-known public wireless datasets, including the gesture dataset, the activity dataset, and the gait dataset. Experimental results show that the sensing accuracy of Co-Sense is more than 10% higher than that of the most advanced wireless sensing models.