【摘要】 基于内生人工智能（AI,artificialintelligence）在大规模复杂异构网络中实现万物智联是6G的重要特征之一。联邦学习（FL, federated learning）因其数据处理本地化这一特有的机器学习架构，被认为是在6G场景中实现分布式泛在智联的重要途径，已成为6G的重要研究方向。为此，首先分析了在未来6G，特别是物联网（IoT,internet of things）场景中引入分布式AI的必要性，以此为基础论述了FL在满足相关6G指标要求的潜力，并从架构设计、资源利用、数据传输、隐私保护、服务提供角度综述了FL如何赋能6G网络，最后给出了FL赋能6G研究存在的一些关键挑战和未来有价值的研究方向。更多还原

【Abstract】 It is an important feature of the 6G that how to realize everything interconnection through large-scale complex heterogeneous networks based on native artificial intelligence(AI). Thanks to the distinct machine learning architecture of data processing locally, federated learning(FL) is regarded as one of the promising solutions to incorporate distributed AI in 6G scenarios, and has become a critical research direction of 6G. Therefore, the necessity of introducing distributed AI into the future 6G especially for internet of things(IoT) scenarios was analyzed. And then, the potentials of FL in meeting the 6G requirements were discussed, and the state-of-the-arts of FL related technologies such as architecture design, resource utilization, data transmission, privacy protection, and service provided for 6G were investigated. Finally, several key technical challenges and potential valuable research directions for FL-empowered 6G were put forward.更多还原