Pranvera Kortoçi, Yilei Liang, Pengyuan Zhou, Lik-Hang Lee, Abbas Mehrabi...
TL;DR该论文提出了一个新的方法，通过使用生成对抗网络（GANs）的隐私保护属性来在用户设备上本地训练 ML 模型，并使用分割学习（SL）在设备之间共享计算负载，从而保护数据隐私且具有短训练时间和模型准确性。
Abstract
mobile devices and the immense amount and variety of data they generate are
key enablers of machine learning (ML)-based applications. Traditional ML
techniques have shifted toward new paradigms such as federated