Fast Adapting Few-Shot Federated Learning System for Human Activity Recognition

The advent of smart devices has revolutionized our daily lives, especially through mobile sensing, with Human Activity Recognition (HAR) emerging as a key area of interest. Its applications span healthcare, indoor localization, and smart environments. Despite its promise, HAR's deployment faces numerous obstacles, such as personalization loss, scarce labeling, long training time, and poor edge device adaptation. Current research often addresses only a subset of these challenges, leaving a gap for a holistic solution. In response, we introduce Meta-Sense-Federated-Learning (MSFL), an innovative federated sensing system designed to personalize deep sensing models to individual users in a shorter time. MSFL leverages meta-learning to navigate data diversity, integrating physical principles to mitigate labeling biases and support real-world applications with minimal user input. Our framework synergizes a server-side model with mobile user engagement, where meta-learning crafts tasks that refine the base model and recalibrate it for novel scenarios. MSFL stands out by extracting uniform activity insights from varied Inertial Measurement Unit (IMU) data, employing data augmentation aligned with physical laws. This approach requires minimal calibration, addressing mobile sensing data's heterogeneity effectively. Users get their personalized model in a short time without having to spend extra time on the federal process. Our empirical findings underscore MSFL's superiority, showcasing a significant boost in model accuracy—over 10% higher than traditional techniques under conditions of limited labels, and 11% higher than MAML in the cross-dataset test. Furthermore, MSFL achieves local adaptation more rapidly, cutting down time expenditure by approximately 30%.