论文标题
基于政策的联邦学习
Policy-Based Federated Learning
论文作者
论文摘要
在本文中，我们介绍了Polifl，这是一个分散的基于边缘的框架，该框架支持联合学习的异质隐私政策。我们对三种用例评估了我们的系统，这些用例训练使用手机收集的敏感用户数据 - 预测性文本，图像分类和通知参与预测 - 在Raspberry Pi Edge设备上。我们发现，Polifl能够在合理的资源和时间预算中进行准确的模型培训和推断，同时还可以执行异质的隐私政策。
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.
