One of the major challenges in Human Activity Recognition (HAR) based on machine learning is the scarcity of labeled data. Indeed, collecting a sufficient amount of training data to build a reliable recognition problem is often prohibitive. Among the many solutions in the literature to mitigate this issue, collaborative learning is emerging as a promising direction to distribute the annotation burden over multiple users that cooperate to build a shared recognition model. One of the major issues of existing methods is that they assume a static activity model with a fixed set of target activities. In this paper, we propose a novel approach that is based on Growing When Required (GWR) neural networks. A GWR network continuously adapts itself according to the input training data, and hence it is particularly suited when the users share heterogeneous sets of activities. Like in federated learning, for the sake of privacy preservation, each user contributes to the global activity classifier by sharing personal model parameters, and not by directly sharing data. In order to further mitigate privacy threats, we implement a strategy to avoid releasing model parameters that may indirectly reveal information about activities that the user specifically marked as private. Our results on two well-known publicly available datasets show the effectiveness and the flexibility of our approach.

中文翻译：

---

具有异构活动集和隐私偏好的协作活动识别

基于机器学习的人类活动识别 (HAR) 的主要挑战之一是标记数据的稀缺性。事实上，收集足够数量的训练数据来构建可靠的识别问题通常是令人望而却步的。在文献中缓解这个问题的众多解决方案中，协作学习正在成为将注释负担分配给合作构建共享识别模型的多个用户的有希望的方向。现有方法的主要问题之一是它们假设具有一组固定目标活动的静态活动模型。在本文中，我们提出了一种基于需要时增长 (GWR) 神经网络的新方法。GWR 网络根据输入的训练数据不断调整自身，因此它特别适用于用户共享异构活动集的情况。与联邦学习一样，为了保护隐私，每个用户通过共享个人模型参数而不是直接共享数据为全局活动分类器做出贡献。为了进一步减轻隐私威胁，我们实施了一项策略，以避免发布可能间接泄露用户明确标记为隐私的活动信息的模型参数。我们在两个众所周知的公开可用数据集上的结果显示了我们方法的有效性和灵活性。为了进一步减轻隐私威胁，我们实施了一项策略，以避免发布可能间接泄露用户明确标记为隐私的活动信息的模型参数。我们在两个众所周知的公开可用数据集上的结果显示了我们方法的有效性和灵活性。为了进一步减轻隐私威胁，我们实施了一项策略，以避免发布可能间接泄露用户明确标记为隐私的活动信息的模型参数。我们在两个众所周知的公开可用数据集上的结果显示了我们方法的有效性和灵活性。