The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavior. To speed up the learning, several researchers designed collaborative learning systems that use data from multiple users. However, disclosing the daily activities of an elderly or disabled user raises privacy concerns. In this paper, we use state-of-the-art deep neural network-based techniques to learn predictive human activity models in the local, centralized, and federated learning settings. A novel aspect of our work is that we carefully track the temporal evolution of the data available to the learner and the data shared by the user. In contrast to previous work where users shared all their data with the centralized learner, we consider users that aim to preserve their privacy. Thus, they choose between approaches in order to achieve their goals of predictive accuracy while minimizing the shared data. To help users make decisions before disclosing any data, we use machine learning to predict the degree to which a user would benefit from collaborative learning. We validate our approaches on real-world data.

翻译：由残疾人或老年人从事的日常活动可以通过智能环境进行监测,获得的数据可用于学习用户行为的预测模型。为了加快学习速度,一些研究人员设计了使用多个用户数据的协作学习系统。然而,披露老年人或残疾用户的日常活动引起了隐私问题。在本文中,我们使用最先进的深层神经网络技术在当地、集中和联合学习环境中学习预测人类活动模型。我们工作的一个新方面是,我们仔细跟踪学习者掌握的数据和用户共享的数据的时间演变情况。与以往用户与集中学习者分享其所有数据的工作相比,我们考虑用户旨在保护其隐私。因此,他们选择两种方法,以便实现预测准确性目标,同时尽量减少共享数据。为了帮助用户在披露任何数据之前作出决定,我们使用机器学习来预测用户从协作学习中受益的程度。我们验证了我们关于现实世界数据的方法。