In this paper we present emph{PoliFL}, a decentralized, edge-based framework
that supports heterogeneous privacy policies for federated learning. We
evaluate our system on three use cases that train models with sensitive user
data collected by mobile phones — predictive text, image classification, and
notification engagement prediction — on a Raspberry~Pi edge device. We find
that PoliFL is able to perform accurate model training and inference within
reasonable resource and time budgets while also enforcing heterogeneous privacy

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