Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead. Decentralized FL (DFL) is a decentralized network architecture that eliminates the need for a central server in contrast to centralized FL (CFL). DFL enables direct communication between clients, resulting in significant savings in communication resources. In this paper, a comprehensive survey and profound perspective are provided for DFL. First, a review of the methodology, challenges, and variants of CFL is conducted, laying the background of DFL. Then, a systematic and detailed perspective on DFL is introduced, including iteration order, communication protocols, network topologies, paradigm proposals, and temporal variability. Next, based on the definition of DFL, several extended variants and categorizations are proposed with state-of-the-art (SOTA) technologies. Lastly, in addition to summarizing the current challenges in the DFL, some possible solutions and future research directions are also discussed.

翻译：联邦学习（FL）因其能够共享知识的同时维护用户数据、保护隐私、提高学习效率并降低通信开销而受到广泛关注。去中心化联邦学习（DFL）是一种去中心化网络架构，与集中式联邦学习（CFL）相比，它消除了对中央服务器的需求。DFL支持客户端之间的直接通信，从而显著节省通信资源。本文对DFL进行了全面综述与深刻展望。首先，回顾了CFL的方法论、挑战及变体，为DFL奠定背景。接着，系统性地详细阐述了DFL的视角，包括迭代顺序、通信协议、网络拓扑、范式提案及时间变异性。随后，基于DFL的定义，结合最新技术（SOTA）提出了若干扩展变体及其分类。最后，除总结DFL当前面临的挑战外，还探讨了可能的解决方案与未来研究方向。