Traditional machine learning is centralized in the cloud (data centers). Recently, the security concern and the availability of abundant data and computation resources in wireless networks are pushing the deployment of learning algorithms toward the network edge. This has led to the emergence of a fast growing area, called edge learning, which integrates two originally decoupled areas: wireless communication and machine learning. It is widely expected that the advancements in edge learning would provide a platform for implementing edge artificial intelligence (AI) in 5G-and-Beyond systems and solving large-scale problems in our society ranging from autonomous driving to personalized healthcare. A typical edge learning framework (e.g., federated learning) features distributed learning over many wireless devices as coordinated by edge servers to cooperatively train a large-scale AI model using local data and CPUs/GPUs. The iterative learning process involves repeated downloading and uploading of high-dimensional (millions to billions) model parameters or their updates by tens to hundreds of devices. This will generate enormous data traffic, placing a heavy burden on the already congested radio access networks. The training problem cannot be efficiently solved using traditional wireless techniques targeting rate maximization and decoupled from learning. Achieving the goal of edge learning with high communication efficiencies calls for the design of new wireless techniques based on a communication-and-learning integration approach.

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客座社论：高效边缘学习的通信技术

传统的机器学习集中在云（数据中心）中。最近，无线网络中的安全问题以及大量数据和计算资源的可用性将学习算法的部署推向了网络边缘。这导致出现了一个快速发展的领域，称为边缘学习，该领域整合了两个最初分离的领域：无线通信和机器学习。人们普遍认为，边缘学习的进步将为在5G-Beyond系统中实施边缘人工智能（AI）和解决我们社会中从自动驾驶到个性化医疗的大规模问题提供一个平台。典型的边缘学习框架（例如，联合学习）的特点是在许多无线设备上进行分布式学习，并由边缘服务器进行协调，以使用本地数据和CPU / GPU协同训练大规模AI模型。迭代学习过程涉及数十个设备到数百个设备的高维（数百万至数十亿）模型参数的重复下载和上传或更新。这将产生巨大的数据流量，给早已拥塞的无线电接入网络带来沉重负担。使用目标速率最大化和与学习脱钩的传统无线技术无法有效解决训练问题。为了实现具有高通信效率的边缘学习的目标，需要设计一种基于通信和学习集成方法的新型无线技术。