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：在过去十年中，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关注。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，其中由中心实体创建全局模型。然而，集中式方法会因瓶颈问题导致延迟增加、系统故障的脆弱性增高，并引发对负责全局模型创建的实体的可信度担忧。去中心化联邦学习（DFL）应运而生，通过促进去中心化的模型聚合并减少对集中式架构的依赖来应对上述问题。然而，尽管在DFL领域已有相关工作，现有文献仍未（i）研究区分DFL与CFL的主要方面；（ii）分析用于创建和评估新解决方案的DFL框架；以及（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、安全方法和关键性能指标等方面识别并分析了DFL的主要基础。此外，本文探讨了现有优化关键DFL基础的机制。随后，对当前DFL框架的最相关特征进行了综述与比较。接着，分析了最常用的DFL应用场景，并根据先前定义的基础与框架识别相关解决方案。最后，研究现有DFL解决方案的演进过程，以提供一系列趋势、经验教训和开放挑战。