One of the major open problems in sensor-based Human Activity Recognition (HAR) is the scarcity of labeled data. Among the many solutions to address this challenge, semi-supervised learning approaches represent a promising direction. However, their centralised architecture incurs in the scalability and privacy problems that arise 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 (i.e., they assume a fully supervised setting). In this work, we propose FedAR: a novel hybrid method for HAR that combines semi-supervised and federated learning to take advantage of the strengths of both approaches. FedAR 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. FedAR also includes a transfer learning strategy to fine-tune the global model on each user. We evaluated our method on two public datasets, showing that FedAR reaches recognition rates and personalization capabilities similar to state-of-the-art FL supervised approaches. As a major advantage, FedAR 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.

翻译：基于传感器的人类活动识别（HAR）面临的主要开放性问题之一是标注数据的稀缺性。针对这一挑战的众多解决方案中，半监督学习方法代表了一个有前景的方向。然而，其集中式架构在涉及大量用户时会引发可扩展性与隐私问题。联邦学习（FL）是解决这些问题的潜在范式。然而，现有针对HAR提出的FL方法均假设参与用户始终能够获取标签以训练其本地模型（即假设完全监督的学习环境）。本研究提出FedAR：一种结合半监督学习与联邦学习的混合方法，以融合两种范式的优势。FedAR通过主动学习与标签传播技术半自动标注本地未标注传感器数据流，并依托联邦学习以可扩展且保护隐私的方式构建全局活动模型。该方法还包含迁移学习策略，用于针对各用户对全局模型进行微调。我们在两个公开数据集上评估了该方法，结果表明FedAR在识别率与个性化能力方面达到了与最先进的监督式联邦学习方法相当的水平。FedAR的核心优势在于：仅需极少量的标注数据即可初始化预训练模型，且主动学习所需的交互量在使用过程中快速递减，从而为HAR的数据稀缺问题提供了高效且可扩展的解决方案。