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。

联邦学习：最新进展与应用的前沿综述

Federated Learning: A Cutting-Edge Survey of the Latest Advancements and  Applications

Fog computing has emerged as a promising technology that can bring the cloud
applications closer to the physical IoT devices at the network edge. While it
is widely known what cloud computing is, and how data centers can build the
cloud infrastructure and how applications can make use of this infrastructure,
there is no common picture on what fog computing and a fog node, as its main
building block, really is. One of the first attempts to define a fog node was
made by Cisco, qualifying a fog computing system as a mini-cloud, located at
the edge of the network and implemented through a variety of edge devices,
interconnected by a variety, mostly wireless, communication technologies. Thus,
a fog node would be the infrastructure implementing the said mini-cloud. Other
proposals have their own definition of what a fog node is, usually in relation
to a specific edge device, a specific use case or an application. In this
paper, we first survey the state of the art in technologies for fog computing
nodes as building blocks of fog computing, paying special attention to the
contributions that analyze the role edge devices play in the fog node
definition. We summarize and compare the concepts, lessons learned from their
implementation, and show how a conceptual framework is emerging towards a
unifying fog node definition. We focus on core functionalities of a fog node as
well as in the accompanying opportunities and challenges towards their
practical realization in the near future.

该论文介绍了雾计算的发展和定义，着重关注了雾节点的核心功能及机会和挑战，并比较了不同提议的概念和实现中得到的经验教训。