In the last decade, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i) studied the main aspects differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and evaluate new solutions; and (iii) reviewed application scenarios using DFL. Thus, this article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators. Additionally, the paper at hand explores existing mechanisms to optimize critical DFL fundamentals. Then, the most relevant features of the current DFL frameworks are reviewed and compared. After that, it analyzes the most used DFL application scenarios, identifying solutions based on the fundamentals and frameworks previously defined. Finally, the evolution of existing DFL solutions is studied to provide a list of trends, lessons learned, and open challenges.

翻译：过去十年间，联邦学习（FL）在无需共享敏感数据的情况下训练协作模型方面日益受到重视。自诞生以来，集中式联邦学习（CFL）一直是文献中最常见的方法，其中中央实体负责创建全局模型。然而，集中式方法会因瓶颈问题导致延迟增加、系统故障脆弱性升高，并引发对全局模型创建实体的信任问题。去中心化联邦学习（DFL）应运而生，通过促进去中心化模型聚合并减少对集中式架构的依赖来应对这些挑战。但尽管DFL领域已有诸多工作，现有文献仍未能：（i）研究区分DFL与CFL的核心要素；（ii）分析用于创建和评估新方案的DFL框架；（iii）综述采用DFL的应用场景。因此，本文从联邦架构、拓扑结构、通信机制、安全方法及关键性能指标等方面，识别并分析了DFL的主要基础原理。此外，本文探讨了优化DFL关键基础原理的现有机制，继而综述并比较了当前DFL框架的最相关特性。随后，本文分析了最常用的DFL应用场景，基于先前定义的基础原理与框架识别出相应解决方案。最后，通过研究现有DFL解决方案的演进过程，本文提出了趋势清单、经验教训及待解决的开放挑战。