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.

Unmanned surface vessel (USV) has been widely used in various fields due to its autonomous advantages, and path planning is a crucial technology for autonomy. However, using global path planning alone cannot avoid moving obstacles, while using local path planning alone may lead to falling into local minima and fail to reach the target. Therefore, this article proposed the dynamic target artificial potential field (DTAPF) method which use a dynamic point that follows the global path generated by the A* algorithm as the target point of the artificial potential field (APF). In addition, in order to improve response time and safety of unmanned surface vessel (USV) navigation of the traditional centralized path planning methods, we proposed an edge computing architecture for global path planning and an offset guidance method to avoid moving obstacles while confirming to the collision regulations (CORLEGs). The experimental results show that, using the method proposed in this article, USV can reach the target in an environment with moving obstacles with high probability (about 99.4%), and compared to the traditional APF algorithm, our method can reduce collision probability by 71% with almost no increase in average path length and average navigation time. Besides, our architecture has much lower computing delay than local computing, and also lower than cloud computing.

For embracing the ubiquitous Internet‐of‐Things (IoT) devices, edge computing and Network Function Virtualization (NFV) have been enabled in branch offices and homes in the form of virtual Customer‐Premises Equipment (vCPE). A Service Provider (SP) deploys vCPE instances as Virtual Network Functions (VNFs) on top of generic physical Customer‐Premises Equipment (pCPE) to ease administration. Upon a usage surge of IoT devices at a certain part of the network, vCPU, memory, and other resource limitations of a single pCPE node make it difficult to add new services handling the high demand. In this paper, we present IoT‐B&B, a novel architecture featuring resource sharing of pCPE nodes. When a pCPE node has sharable resources available, the SP will utilize its free resources as a “bed‐and‐breakfast” place to deploy vCPE instances in need. A placement algorithm is also presented to assign vCPE instances to a cost‐efficient pCPE node. By keeping vCPE instances at the network edge, their costs of hosting are reduced. Meanwhile, the transmission latencies are maintained at acceptable levels for processing real‐time data burst from IoT devices. The traffic load to the remote, centralized cloud can be substantially reduced.