论文标题
在非IID设置中的联合学习得到了差异私人合成数据的帮助
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data
论文作者
论文摘要
联合学习（FL）是一个促进隐私的框架，它使潜在的大量客户能够协作培训机器学习模型。在FL系统中，服务器通过收集和汇总客户的模型更新在客户端数据保持本地和私有时来协调协作。当本地数据是异质的时，会出现联合学习的主要挑战 - 与在客户端相同分布的方案相比，学到的全球模型的性能可能会严重恶化的设置。在本文中，我们提出了FIDDPMS（联合差异性私有方式共享），这是一种FL算法，在该算法中，客户在该算法中部署各种自动编码器来增强本地数据集，并使用使用受信任的服务器传达的潜在数据表示的数据合成的数据来增强本地数据集。这种增强可以缓解客户对客户的数据异质性的影响，而不会损害隐私。我们对深层图像分类任务的实验表明，FedDPMS优于专门为异构数据设置设计的最先进的FL方法。
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.
