  Pervasive computing applications commonly involve user's personal smartphones
collecting data to influence application behavior. Applications are often
backed by models that learn from the user's experiences to provide personalized
and responsive behavior. While models are often pre-trained on massive
datasets, federated learning has gained attention for its ability to train
globally shared models on users' private data without requiring the users to
share their data directly. However, federated learning requires devices to
collaborate via a central server, under the assumption that all users desire to
learn the same model. We define a new approach, opportunistic federated
learning, in which individual devices belonging to different users seek to
learn robust models that are personalized to their user's own experiences.
However, instead of learning in isolation, these models opportunistically
incorporate the learned experiences of other devices they encounter
opportunistically. In this paper, we explore the feasibility and limits of such
an approach, culminating in a framework that supports encounter-based pairwise
collaborative learning. The use of our opportunistic encounter-based learning
amplifies the performance of personalized learning while resisting overfitting
to encountered data.
