联合学习的进步:模式、方法和隐私

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-06-01 DOI:10.1145/3664650
Huiming Chen, Huandong Wang, Qingyue Long, Depeng Jin, Yong Li
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引用次数: 0

摘要

联合学习(FL)是解决日益增长的隐私和安全问题的一种有前途的技术。其主要内容是在不上传任何敏感数据的情况下,在分布式客户端之间合作学习模型。在本文中,我们对相关工作进行了全面回顾,遵循发展脉络,从理论和应用的角度深入挖掘了 FL 背后的关键技术。具体来说,我们首先根据 FL 系统的网络拓扑结构对 FL 架构的现有工作进行了分类,并进行了详细的分析和总结。接着,我们抽象出当前的应用问题,总结出通用技术,并将应用问题框定到 FL 基础模型的一般范式中。此外,我们还提出了通过 FL 进行模型训练的解决方案。我们总结分析了现有的 FedOpt 算法,深入揭示了许多一阶算法的算法开发原理,提出了更具普适性的算法设计框架。通过这些框架的实例化,可以简单地开发出 FedOpt 算法。由于隐私和安全是 FL 的基本要求,我们提供了现有的攻击场景和防御方法。据我们所知,我们是第一批回顾理论方法并提出我们的策略的人,因为很少有著作调查理论方法。我们的调查旨在激励开发高性能、保护隐私和安全的方法,以便将 FL 集成到现实世界的应用中。
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Advancements in Federated Learning: Models, Methods, and Privacy

Federated learning (FL) is a promising technique for resolving the rising privacy and security concerns. Its main ingredient is to cooperatively learn the model among the distributed clients without uploading any sensitive data. In this paper, we conducted a thorough review of the related works, following the development context and deeply mining the key technologies behind FL from the perspectives of theory and application. Specifically, we first classify the existing works in FL architecture based on the network topology of FL systems with detailed analysis and summarization. Next, we abstract the current application problems, summarize the general techniques and frame the application problems into the general paradigm of FL base models. Moreover, we provide our proposed solutions for model training via FL. We have summarized and analyzed the existing FedOpt algorithms, and deeply revealed the algorithmic development principles of many first-order algorithms in depth, proposing a more generalized algorithm design framework. With the instantiation of these frameworks, FedOpt algorithms can be simply developed. As privacy and security is the fundamental requirement in FL, we provide the existing attack scenarios and the defense methods. To the best of our knowledge, we are among the first tier to review the theoretical methodology and propose our strategies since there are very few works surveying the theoretical approaches. Our survey targets motivating the development of high-performance, privacy-preserving, and secure methods to integrate FL into real-world applications.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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