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)是解决不断上升的隐私和安全问题的一个很有希望的技术,其主要内容是合作在分布式客户中学习模型,而不上传任何敏感数据;在本文件中,我们根据发展背景对相关工作进行了彻底审查,并从理论和实践角度深入挖掘FL背后的关键技术;具体地说,我们首先根据FL系统网络地形学对FL结构中的现有工程进行分类,进行详细分析和总结;接着,我们将目前的应用问题归纳为当前的应用问题,总结一般技术,并将应用问题纳入FL基础模型的一般范例;此外,我们通过FL提供示范培训的拟议解决办法。我们已经对现有的FedOpt算法进行了总结和分析,深入地揭示了许多一阶算法的算法发展原则,提出了更普遍的算法设计框架。我们根据这些框架,对FedOpt算法进行了即时速化。由于隐私和安全是FL的基本要求,我们提供了现有的攻击情景和防御方法。我们所了解的最好的是,我们属于第一层次,要审查理论性方法,并提出了我们从高保度调查以来的理论性方法,提出了发展方法。