Traditionally, deep learning practitioners would bring data into a central repository for model training and inference. Recent developments in distributed learning, such as federated learning and deep learning as a service (DLaaS) do not require centralized data and instead push computing to where the distributed datasets reside. These decentralized training schemes, however, introduce additional security and privacy challenges. This survey first structures the field of distributed learning into two main paradigms and then provides an overview of the recently published protective measures for each. This work highlights both secure training methods as well as private inference measures. Our analyses show that recent publications while being highly dependent on the problem definition, report progress in terms of security, privacy, and efficiency. Nevertheless, we also identify several current issues within the private and secure distributed deep learning (PSDDL) field that require more research. We discuss these issues and provide a general overview of how they might be resolved.

中文翻译：

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私有和安全的分布式深度学习：一项调查


传统上，深度学习从业者会将数据引入中央存储库，用于模型训练和推理。分布式学习的最新发展，例如联合学习和深度学习即服务 （DLaaS），不需要集中式数据，而是将计算推向分布式数据集所在的位置。然而，这些分散的训练计划带来了额外的安全和隐私挑战。本调查首先将分布式学习领域分为两个主要范式，然后概述了最近发布的针对每个范式的保护措施。这项工作重点介绍了安全训练方法和私有推理措施。我们的分析表明，最近的出版物虽然高度依赖于问题定义，但报告了安全性、隐私和效率方面的进展。尽管如此，我们还确定了私有和安全分布式深度学习 （PSDDL） 领域中需要更多研究的几个当前问题。我们将讨论这些问题，并简要概述如何解决这些问题。