Federated learning (FL) is a privacy-promoting framework that enables potentially large number of clients to collaboratively train machine learning models. In a FL system, a server coordinates the collaboration by collecting and aggregating clients' model updates while the clients' data remains local and private. A major challenge in federated learning arises when the local data is heterogeneous -- the setting in which performance of the learned global model may deteriorate significantly compared to the scenario where the data is identically distributed across the clients. In this paper we propose FedDPMS (Federated Differentially Private Means Sharing), an FL algorithm in which clients deploy variational auto-encoders to augment local datasets with data synthesized using differentially private means of latent data representations communicated by a trusted server. Such augmentation ameliorates effects of data heterogeneity across the clients without compromising privacy. Our experiments on deep image classification tasks demonstrate that FedDPMS outperforms competing state-of-the-art FL methods specifically designed for heterogeneous data settings.

本文提出了基于差分隐私均值共享的联邦学习算法FedDPMS，该算法使用变分自编码器在保护隐私的前提下，增强本地数据集合成多样化的数据以避免数据异构性对于联邦学习的负面影响，实验表明FedDPMS算法比其他面向异构数据的联邦学习算法性能更佳，特别适用于深度图像分类任务。

非IID环境下辅以差分隐私合成数据的联邦学习