Policy-Based Federated Learning

Kleomenis Katevas; Eugene Bagdasaryan; Jason Waterman; Mohamad Mounir Safadieh; Eleanor Birrell; Hamed Haddadi; Deborah Estrin

arXiv · 2003.06612

Policy-Based Federated Learning

Kleomenis Katevas·Eugene Bagdasaryan·Jason Waterman·Mohamad Mounir Safadieh·Eleanor Birrell·Hamed Haddadi·Deborah Estrin

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Abstract

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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Kleomenis Katevas, Eugene Bagdasaryan, Jason Waterman, Mohamad Mounir Safadieh, Eleanor Birrell, Hamed Haddadi, Deborah Estrin. 2020-03-14. Policy-Based Federated Learning. https://arxiv.org/abs/2003.06612

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