Policy-Based Federated Learning

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 policies.