Federated learning effectively addresses issues such as data privacy by
collaborating across participating devices to train global models. However,
factors such as network topology and device computing power can affect its
training or communication process in complex network environments. A new
network architecture and paradigm with computing-measurable, perceptible,
distributable, dispatchable, and manageable capabilities, computing and network
convergence (CNC) of 6G networks can effectively support federated learning
training and improve its communication efficiency. By guiding the participating
devices' training in federated learning based on business requirements,
resource load, network conditions, and arithmetic power of devices, CNC can
reach this goal. In this paper, to improve the communication efficiency of
federated learning in complex networks, we study the communication efficiency
optimization of federated learning for computing and network convergence of 6G
networks, methods that gives decisions on its training process for different
network conditions and arithmetic power of participating devices in federated
learning. The experiments address two architectures that exist for devices in
federated learning and arrange devices to participate in training based on
arithmetic power while achieving optimization of communication efficiency in
the process of transferring model parameters. The results show that the method
we proposed can (1) cope well with complex network situations (2) effectively
balance the delay distribution of participating devices for local training (3)
improve the communication efficiency during the transfer of model parameters
(4) improve the resource utilization in the network.

通过引导参与联邦学习的设备基于业务需求、资源负载、网络状况和设备的算术能力，计算和网络融合（CNC）可以提高联邦学习在复杂网络环境中的通信效率，并通过优化模型参数传输过程中的通信效率来解决复杂网络情况下的延迟分布和网络资源利用率的问题。

优化联邦学习在 6G 网络的计算和网络融合中的通信效率

Communication Efficiency Optimization of Federated Learning for  Computing and Network Convergence of 6G Networks

Massive emerging applications are driving demand for the ubiquitous
deployment of computing power today. This trend not only spurs the recent
popularity of the \emph{Computing and Network Convergence} (CNC), but also
introduces an urgent need for the intelligentization of a management platform
to coordinate changing resources and tasks in the CNC. Therefore, in this
article, we present the concept of an intelligence-endogenous management
platform for CNCs called \emph{CNC brain} based on artificial intelligence
technologies. It aims at efficiently and automatically matching the supply and
demand with high heterogeneity in a CNC via four key building blocks, i.e.,
perception, scheduling, adaptation, and governance, throughout the CNC's life
cycle. Their functionalities, goals, and challenges are presented. To examine
the effectiveness of the proposed concept and framework, we also implement a
prototype for the CNC brain based on a deep reinforcement learning technology.
Also, it is evaluated on a CNC testbed that integrates two open-source and
popular frameworks (OpenFaas and Kubernetes) and a real-world business dataset
provided by Microsoft Azure. The evaluation results prove the proposed method's
effectiveness in terms of resource utilization and performance. Finally, we
highlight the future research directions of the CNC brain.

基于人工智能技术，本文提出了一种用于计算和网络融合的智能内生管理平台 ——CNC 大脑，通过感知、调度、适应和治理等四个关键构建模块，在整个计算和网络融合生命周期中实现高异构性的供需匹配，通过在 OpenFaas、Kubernetes 和 Microsoft Azure 的真实业务数据集上进行评估，证明了该方法在资源利用和性能方面的有效性，并提出了 CNC 大脑的未来研究方向。

计算与网络融合的智能内源化管理平台

Intelligence-Endogenous Management Platform for Computing and Network  Convergence

As computing power is becoming the core productivity of the digital economy
era, the concept of Computing and Network Convergence (CNC), under which
network and computing resources can be dynamically scheduled and allocated
according to users' needs, has been proposed and attracted wide attention.
Based on the tasks' properties, the network orchestration plane needs to
flexibly deploy tasks to appropriate computing nodes and arrange paths to the
computing nodes. This is a orchestration problem that involves resource
scheduling and path arrangement. Since CNC is relatively new, in this paper, we
review some researches and applications on CNC. Then, we design a CNC
orchestration method using reinforcement learning (RL), which is the first
attempt, that can flexibly allocate and schedule computing resources and
network resources. Which aims at high profit and low latency. Meanwhile, we use
multi-factors to determine the optimization objective so that the orchestration
strategy is optimized in terms of total performance from different aspects,
such as cost, profit, latency and system overload in our experiment. The
experiments shows that the proposed RL-based method can achieve higher profit
and lower latency than the greedy method, random selection and
balanced-resource method. We demonstrate RL is suitable for CNC orchestration.
This paper enlightens the RL application on CNC orchestration.

本文介绍了计算与网络融合的概念，提出了基于强化学习的计算与网络编排方法，通过实验显示该方法可以比贪婪算法、随机选择和均衡资源算法实现更高的利润和更低的延迟，并证明了强化学习在计算与网络编排上的适用性。