Private and Secure Distributed Deep Learning: A Survey

doi:10.1145/3703452

2024-12-09

DOI：10.1145/3703452

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摘要

En 中文

传统上，深度学习从业者会将数据引入中央存储库以进行模型训练和推理。分布式学习（如联邦学习和深度学习即服务DLaaS）的最新进展无需集中数据，而是将计算推送到分布式数据集所在的位置。然而，这些去中心化训练方案引入了额外的安全和隐私挑战。本综述首先将分布式学习领域结构化为两种主要范式，然后概述了针对每种范式近期发表的防护措施。本研究强调了安全训练方法以及隐私推理措施。我们的分析表明，近期出版物虽然高度依赖于问题定义，但在安全、隐私和效率方面均报告了进展。尽管如此，我们也识别出私有化和安全分布式深度学习（PSDDL）领域内若干当前问题，需要更多研究。我们讨论了这些问题，并提供了关于如何解决它们的总体概述。

Keyword:

Deep learning

privacy

security

distributed learning

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