Personalized Semi-Supervised Federated Learning for Human Activity Recognition
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The most effective data-driven methods for human activities recognition (HAR) are based on supervised learning applied to the continuous stream of sensors data. However, these methods perform well on restricted sets of activities in domains for which there is a fully labeled dataset. It is still a challenge to cope with the intra- and inter-variability of activity execution among different subjects in large scale real world deployment. Semi-supervised learning approaches for HAR have been proposed to address the challenge of acquiring the large amount of labeled data that is necessary in realistic settings. However, their centralised architecture incurs in the scalability and privacy problems when the process involves a large number of users. Federated Learning (FL) is a promising paradigm to address these problems. However, the FL methods that have been proposed for HAR assume that the participating users can always obtain labels to train their local models. In this work, we propose FedHAR: a novel hybrid method for HAR that combines semi-supervised and federated learning. Indeed, FedHAR combines active learning and label propagation to semi-automatically annotate the local streams of unlabeled sensor data, and it relies on FL to build a global activity model in a scalable and privacy-aware fashion. FedHAR also includes a transfer learning strategy to personalize the global model on each user. We evaluated our method on two public datasets, showing that FedHAR reaches recognition rates and personalization capabilities similar to state-of-the-art FL supervised approaches. As a major advantage, FedHAR only requires a very limited number of annotated data to populate a pre-trained model and a small number of active learning questions that quickly decrease while using the system, leading to an effective and scalable solution for the data scarcity problem of HAR.
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