In this paper we present 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 policies.

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