Federated Edge Learning for 6G: Foundations, Methodologies, and Applications

被引：0

作者：

Tao, Meixia ^{[1
]}

Zhou, Yong ^{[2
]}

Shi, Yuanming ^{[2
]}

Lu, Jianmin ^{[3
]}

Cui, Shuguang ^{[4
,5
]}

Lu, Jianhua ^{[6
,7
]}

Letaief, Khaled B. ^{[8
]}

机构：

[1] Shanghai Jiao Tong Univ, Cooperat Medianet Innovat Ctr, Shanghai 200240, Peoples R China

[2] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China

[3] Huawei Technol Co Ltd, Shenzhen 518066, Peoples R China

[4] Chinese Univ Hong Kong, Shenzhen Future Network Intelligence Inst FNii She, Sch Sci & Engn SSE, Shenzhen 518066, Peoples R China

[5] Chinese Univ Hong Kong, Guangdong Prov Key Lab Future Networks Intelligenc, Shenzhen 518066, Peoples R China

[6] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China

[7] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China

[8] Hong Kong Univ Sci & Technol HKUST, Dept Elect & Comp Engn, Hong Kong, Peoples R China

来源：

PROCEEDINGS OF THE IEEE | 2024年

基金：

上海市自然科学基金; 中国国家自然科学基金;

关键词：

Domain-specific optimization; federated edge learning (FEEL); integrated sensing-communication-computation; sixth-generation (6G); task-oriented communications; GENERATIVE ADVERSARIAL NETWORKS; STOCHASTIC GRADIENT DESCENT; THE-AIR COMPUTATION; SEMANTIC COMMUNICATION; MASSIVE MIMO; CONVERGENCE ANALYSIS; MODEL AGGREGATION; BANDWIDTH ALLOCATION; RESOURCE-ALLOCATION; CSI FEEDBACK;

D O I：

10.1109/JPROC.2024.3509739

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Artificial intelligence (AI) is envisioned to be natively integrated into the sixth-generation (6G) mobile networks to support a diverse range of intelligent applications. Federated edge learning (FEEL) emerges as a vital enabler of this vision by leveraging the sensing, communication, and computation capabilities of geographically dispersed edge devices to collaboratively train AI models without sharing raw data. This article explores the pivotal role of FEEL in advancing both the "wireless for AI" and "AI for wireless" paradigms, thereby facilitating the realization of scalable, adaptive, and intelligent 6G networks. We begin with a comprehensive overview of learning architectures, models, and algorithms that form the foundations of FEEL. We, then, establish a novel task-oriented communication principle to examine key methodologies for deploying FEEL in dynamic and resource-constrained wireless environments, focusing on device scheduling, model compression, model aggregation, and resource allocation. Furthermore, we investigate the domain-specific optimizations of FEEL to facilitate its promising applications, ranging from wireless air-interface technologies to mobile and the Internet of Things (IoT) services. Finally, we highlight key future research directions for enhancing the design and impact of FEEL in 6G.

引用

页数：39