With 5G cellular systems being actively deployed worldwide, the research
community has started to explore novel technological advances for the
subsequent generation, i.e., 6G. It is commonly believed that 6G will be built
on a new vision of ubiquitous AI, an hyper-flexible architecture that brings
human-like intelligence into every aspect of networking systems. Despite its
great promise, there are several novel challenges expected to arise in
ubiquitous AI-based 6G. Although numerous attempts have been made to apply AI
to wireless networks, these attempts have not yet seen any large-scale
implementation in practical systems. One of the key challenges is the
difficulty to implement distributed AI across a massive number of heterogeneous
devices. Federated learning (FL) is an emerging distributed AI solution that
enables data-driven AI solutions in heterogeneous and potentially massive-scale
networks. Although it still in an early stage of development, FL-inspired
architecture has been recognized as one of the most promising solutions to
fulfill ubiquitous AI in 6G. In this article, we identify the requirements that
will drive convergence between 6G and AI. We propose an FL-based network
architecture and discuss its potential for addressing some of the novel
challenges expected in 6G. Future trends and key research problems for
FL-enabled 6G are also discussed.

本文提出了一种基于联邦学习的网络架构，旨在通过分布式人工智能解决 6G 中的一些新挑战，包括应用 AI 于无线网络的困难，以及跨异构设备实现分布式 AI 的困难。