In the realm of machine learning (ML) systems featuring client-host connections, the enhancement of privacy security can be effectively achieved through federated learning (FL) as a secure distributed ML methodology. FL effectively integrates cloud infrastructure to transfer ML models onto edge servers using blockchain technology. Through this mechanism, it guarantees the streamlined processing and data storage requirements of both centralized and decentralized systems, with an emphasis on scalability, privacy considerations, and cost-effective communication. In current FL implementations, data owners locally train their models, and subsequently upload the outcomes in the form of weights, gradients, and parameters to the cloud for overall model aggregation. This innovation obviates the necessity of engaging Internet of Things (IoT) clients and participants to communicate raw and potentially confidential data directly with a cloud center. This not only reduces the costs associated with communication networks but also enhances the protection of private data. This survey conducts an analysis and comparison of recent FL applications, aiming to assess their efficiency, accuracy, and privacy protection. However, in light of the complex and evolving nature of FL, it becomes evident that additional research is imperative to address lingering knowledge gaps and effectively confront the forthcoming challenges in this field. In this study, we categorize recent literature into the following clusters: privacy protection, resource allocation, case study analysis, and applications. Furthermore, at the end of each section, we tabulate the open areas and future directions presented in the referenced literature, affording researchers and scholars an insightful view of the evolution of the field.

翻译：在涉及客户端-服务器连接的机器学习系统中，联邦学习作为一种安全的分布式机器学习方法，能够有效增强隐私安全性。联邦学习通过区块链技术将云基础设施与边缘服务器集成，实现机器学习模型的迁移。该机制在确保可扩展性、隐私保护和经济高效通信的前提下，满足了集中式与分布式系统的流式处理和数据存储需求。在当前的联邦学习实现中，数据所有者本地训练模型，随后将权重、梯度和参数等结果上传至云端进行全局模型聚合。这一创新消除了物联网客户端与参与者需直接与云中心传输原始潜在敏感数据的必要性，不仅降低了通信网络成本，还增强了私密数据的保护。本综述分析并比较了近期联邦学习的应用，旨在评估其效率、准确性与隐私保护水平。然而，鉴于联邦学习的复杂性与持续演进特性，显然需要开展更多研究以填补现存知识空白，有效应对这一领域即将面临的挑战。在本研究中，我们将近期文献划分为以下类别：隐私保护、资源分配、案例研究分析与应用。此外，每节末尾均以表格形式列出参考文献中提出的开放领域与未来方向，使研究人员和学者能够深入洞察该领域的演变趋势。