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Pervasive computing applications commonly involve user's personal smartphonescollecting data to influence application behavior. Applications are oftenbacked by models that learn from the user's experiences to provide personalizedand responsive behavior. While models are often pre-trained on massivedatasets, federated learning has gained attention for its ability to trainglobally shared models on users' private data without requiring the users toshare their data directly. However, federated learning requires devices tocollaborate via a central server, under the assumption that all users desire tolearn the same model. We define a new approach, opportunistic federatedlearning, in which individual devices belonging to different users seek tolearn robust models that are personalized to their user's own experiences.However, instead of learning in isolation, these models opportunisticallyincorporate the learned experiences of other devices they encounteropportunistically. In this paper, we explore the feasibility and limits of suchan approach, culminating in a framework that supports encounter-based pairwisecollaborative learning. The use of our opportunistic encounter-based learningamplifies the performance of personalized learning while resisting overfittingto encountered data.

Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications