Policy-Based Federated Learning

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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Cited by in corpus (2)

• From Distributed Machine Learning to Federated Learning: A Survey
• PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments