Guest Editorial: Communication Technologies for Efficient Edge Learning

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.