联邦学习赋能6G网络综述

doi:10.11959/j.issn.2096-3750.2023.00323

物联网学报 ›› 2023, Vol. 7 ›› Issue (2): 50-66.doi: 10.11959/j.issn.2096-3750.2023.00323

联邦学习赋能6G网络综述

耿光磊¹^,², 高博¹^,², 熊轲¹^,², 樊平毅³, 陆杨¹^,², 王煜炜⁴

¹ 北京交通大学计算机与信息技术学院，北京 100044
² 北京交通大学高速铁路网络管理教育部工程研究中心，北京 100044
³ 清华大学北京信息科学与技术国家研究中心，北京 100084
⁴ 中国科学院计算技术研究所，北京 100190

修回日期:2023-01-18 出版日期:2023-06-30 发布日期:2023-06-01
作者简介:耿光磊（1999- ），男，北京交通大学计算机与信息技术学院硕士生，主要研究方向为联邦学习、车联网资源分配等
高博（1984- ），男，博士，北京交通大学计算机与信息技术学院副教授，主要研究方向为无线网络、移动计算、机器学习等
熊轲（1981- ），男，博士，北京交通大学计算机与信息技术学院教授、博士生导师、副院长，主要研究方向为绿色物联网、网络资源优化、网络智能与移动计算等
樊平毅（1965- ），男，博士，清华大学电子工程系教授、博士生导师，主要研究方向为网络信息论、无线通信、大数据理论、分布式机器学习等
陆杨（1992- ），男，博士，北京交通大学计算机与信息技术学院教授，主要研究方向为优化理论、机器学习赋能移动通信系统等
王煜炜（1980- ）男，博士，中国科学院计算技术研究所高级工程师、硕士生导师，主要研究方向为边缘智能、无人系统网络协同、未来网络体系架构等
基金资助:
国家自然科学基金资助项目(61872028);中央高校基本科研业务费资助项目(2021JBM008);中央高校基本科研业务费资助项目(2022JBXT001)

A survey of federated learning for 6G networks

Guanglei GENG¹^,², Bo GAO¹^,², Ke XIONG¹^,², Pingyi FAN³, Yang LU¹^,², Yuwei WANG⁴

¹ School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
² Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China
³ National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
⁴ Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Revised:2023-01-18 Online:2023-06-30 Published:2023-06-01
Supported by:
The National Natural Science Foundation of China(61872028);The Fundamental Research Funds for the Central Universities(2021JBM008);The Fundamental Research Funds for the Central Universities(2022JBXT001)

摘要/Abstract

摘要：

基于内生人工智能（AI, artificial intelligence）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。

关键词: 6G网络, 物联网, 人工智能, 联邦学习

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.

Key words: 6G networks, internet of things, artificial intelligence, federated learning

中图分类号:

TP393
TN92

耿光磊, 高博, 熊轲, 樊平毅, 陆杨, 王煜炜. 联邦学习赋能6G网络综述[J]. 物联网学报, 2023, 7(2): 50-66.

Guanglei GENG, Bo GAO, Ke XIONG, Pingyi FAN, Yang LU, Yuwei WANG. A survey of federated learning for 6G networks[J]. Chinese Journal on Internet of Things, 2023, 7(2): 50-66.

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FL赋能6G	概述	分类
促进灵活架构设计	FL针对6G移动边缘场景灵活设计分布式网络架构	传统网络场景架构改进^[18-24]
		车联网场景架构设计^[25-30]
促进高效资源利用	FL结合决策算法优化6G网络中的分布式资源利用	FL结合传统优化方法^[31-35]
		FL结合DRL算法^[44-48]
促进可靠数据传输	FL在架构、算法层面，降低6G场景中的时延和通信成本	基于全局优化通信指标^[51-55]
		基于参与方优化通信指标^[56-60]
促进安全隐私保护	FL结合隐私保护技术，确保6G分布式场景模型安全传输	FL结合安全多方计算^[62-67]
		FL结合差分隐私^[68-73]
		FL结合同态加密^[74-77]
促进个性服务提供	FL克服6G数据Non-IID分布特点，提供个性化智能服务	基于服务端实现个性化FL^[79-82]
		基于客户端实现个性化FL^[83-92]
		基于其他技术实现个性化FL^[93-100]

联邦学习赋能6G网络综述

A survey of federated learning for 6G networks

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