Democratizing Federated WiFi-Based Human Activity Recognition Using Hypothesis Transfer

Human activity recognition (HAR) is a crucial task in IoT systems with applications ranging from surveillance and intruder detection to home automation and more. Recently, non-invasive HAR utilizing WiFi signals has gained considerable attention due to advancements in ubiquitous WiFi technologies. However, recent studies have revealed significant privacy risks associated with WiFi signals, raising concerns about bio-information leakage. To address these concerns, the decentralized paradigm, particularly federated learning (FL), has emerged as a promising approach for training HAR models while preserving data privacy. Nevertheless, FL models may struggle in end-user environments due to substantial domain discrepancies between the source training data and the target end-user environment. This discrepancy arises from the sensitivity of WiFi signals to environmental changes, resulting in notable domain shifts. As a consequence, FL-based HAR approaches often face challenges when deployed in real-world WiFi environments. Albeit there are pioneer attempts on federated domain adaptation, they typically require non-trivial communication and computation cost, which is prohibitively expensive especially considering edge-based hardware equipment of end-user environment. In this paper, we propose a model to democratize the WiFi-based HAR system by enhancing recognition accuracy in unannotated end-user environments while prioritizing data privacy. Our model leverages the hypothesis transfer and a lightweight hypothesis ensemble to mitigate negative transfer. We prove a tighter theoretical upper bound compared to existing multi-source federated domain adaptation models. Extensive experiments shows our model improves the average accuracy by approximately 10 absolute percentage points in both cross-person and cross-environment settings comparing several state-of-the-art baselines.