The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effective approach, federated learning (FL) is a natural solution for massive user-owned devices in edge computing with distributed and private training data. FL methods based on FedAvg typically follow a naive star topology, ignoring the heterogeneity and hierarchy of the volatile edge computing architectures and topologies in reality. Several other network topologies exist and can address the limitations and bottlenecks of the star topology. This motivates us to survey network topology-related FL solutions. In this paper, we conduct a comprehensive survey of the existing FL works focusing on network topologies. After a brief overview of FL and edge computing networks, we discuss various edge network topologies as well as their advantages and disadvantages. Lastly, we discuss the remaining challenges and future works for applying FL to topology-specific edge networks.

5G/6G应用的超低延迟要求和隐私约束要求在边缘部署分布式机器学习系统。联邦学习 (FL) 以其简单而有效的方法，成为边缘计算中具有分布式和私有训练数据的大量用户拥有的设备的自然解决方案。基于FedAvg的FL方法通常遵循朴素的星形拓扑，忽略现实中易失性边缘计算架构和拓扑的异构性和层次结构。存在其他几种网络拓扑，可以解决星型拓扑的限制和瓶颈。这促使我们研究与网络拓扑相关的 FL 解决方案。在本文中，我们对现有的 FL 工作进行了全面的调查，重点关注网络拓扑。在简要概述 FL 和边缘计算网络之后，我们讨论了各种边缘网络拓扑及其优缺点。最后，我们讨论将 FL 应用到特定拓扑边缘网络的剩余挑战和未来工作。