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）是一种应对日益突出的隐私与安全问题的有前景技术。其核心在于通过分布式客户端协同训练模型，且无需上传任何敏感数据。本文从理论与实践双重视角，沿发展脉络对相关研究进行了系统性梳理，并深入挖掘了联邦学习背后的关键技术。具体而言，我们首先基于联邦系统网络拓扑对现有联邦学习架构相关工作进行分类，并进行了详尽分析与总结。其次，我们抽象出现有应用问题，归纳通用技术，并将应用问题框架化为联邦学习基础模型的通用范式。此外，我们提出了基于联邦学习进行模型训练的解决方案。我们对现有FedOpt算法进行了总结与分析，深入揭示了多种一阶算法的算法发展原理，并提出了一种更具通用性的算法设计框架。基于该框架，我们实例化了FedOpt算法。鉴于隐私与安全是联邦学习的基本要求，我们梳理了现有攻击场景及防御方法。据我们所知，由于现有理论方法综述极少，我们属于首批系统评述理论方法论并自主提出策略的研究者之一。本综述旨在推动高性能、隐私保护且安全的方法发展，以促进联邦学习在实际应用中的落地。