  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.
