Federated learning (FL) is a promising technique for addressing the rising privacy and security issues. 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 both theoretical and practical perspectives. 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. Based on these frameworks, we have instantiated FedOpt algorithms. 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.

翻译：联邦学习（FL）是应对日益增长的隐私与安全问题的一项有前景的技术。其核心在于分布式客户端在不上传任何敏感数据的情况下协作学习模型。本文沿发展脉络对相关研究进行了全面回顾，并从理论与实践两个层面深入挖掘了FL背后的关键技术。具体而言，我们首先基于FL系统的网络拓扑结构对现有FL架构研究成果进行分类，并进行了详细分析与总结。其次，我们抽象了当前的应用问题，归纳了通用技术，并将应用问题框架化为FL基础模型的通用范式。此外，我们提出了利用FL进行模型训练的解决方案。我们总结并分析了现有的FedOpt算法，深入揭示了众多一阶算法的算法发展原理，提出了更为通用的算法设计框架。基于这些框架，我们实例化了FedOpt算法。鉴于隐私与安全是FL的根本要求，我们提供了现有的攻击场景与防御方法。据我们所知，由于极少有研究对理论方法进行综述，我们属于首批回顾理论方法论并提出自身策略的研究者之一。本综述旨在推动开发高性能、隐私保护且安全的方法，以促进FL在现实应用中的整合。