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.

在机器学习系统中，通过联邦学习作为一个安全的分布式机器学习方法，有效提升隐私安全；它利用区块链技术将机器学习模型转移到边缘服务器，通过这种机制保证了集中式和分散式系统的高效处理和数据存储需求，强调可伸缩性、隐私考虑和成本有效的通信。本文对最近的联邦学习应用进行了分析和比较，以评估其效率、准确性和隐私保护。然而，鉴于联邦学习复杂且不断演化的特点，明确需要进一步研究来解决知识间的差距并有效应对该领域面临的挑战。在本文中，我们将最近的文献分为以下几个类别：隐私保护、资源分配、案例研究分析和应用，此外，在每个章节的末尾，我们还对参考文献中提出的开放领域和未来方向进行了总结，为研究人员提供了领域演进的深入视角。