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.
@article{arxiv.2003.06612,
title = {Policy-Based Federated Learning},
author = {Kleomenis Katevas and Eugene Bagdasaryan and Jason Waterman and Mohamad Mounir Safadieh and Eleanor Birrell and Hamed Haddadi and Deborah Estrin},
journal= {arXiv preprint arXiv:2003.06612},
year = {2021}
}