Hierarchical Federated Learning: Architecture, Challenges, and Its Implementation in Vehicular Networks

doi:10.12142/ZTECOM.202301005

ZTE Communications ›› 2023, Vol. 21 ›› Issue (1): 38-45.DOI: 10.12142/ZTECOM.202301005

收稿日期:2022-11-04 出版日期:2023-03-25 发布日期:2024-03-15

Hierarchical Federated Learning: Architecture, Challenges, and Its Implementation in Vehicular Networks

YAN Jintao¹, CHEN Tan¹, XIE Bowen¹, SUN Yuxuan², ZHOU Sheng¹(), NIU Zhisheng¹

^1.Tsinghua University, Beijing 100084, China
^2.Beijing Jiaotong University, Beijing 100044, China

Received:2022-11-04 Online:2023-03-25 Published:2024-03-15
About author:YAN Jintao is a PhD student at Tsinghua University, China. His research interests include federated learning and vehicular edge computing and vehicular networks.
CHEN Tan is a PhD student at Tsinghua University, China. His research interests include federated learning and vehicular networks.
XIE Bowen is a PhD student at Tsinghua University, China. His research interests include federated learning and vehicular networks.
SUN Yuxuan is an associate professor with the School of Electronic and Information Engineering, Beijing Jiaotong University, China and was previously a postdoctoral researcher with Tsinghua University, China. Her research interests include edge computing and edge learning.
ZHOU Sheng (sheng.zhou@tsinghua.edu.cn) is an associate professor with the Department of Electronic Engineering, Tsinghua University, China. His research interests include vehicular networks, mobile edge computing, and green wireless communications.
NIU Zhisheng is a professor with the Department of Electronic Engineering, Tsinghua University, China. His major research interests include queueing theory, traffic engineering, radio resource management of wireless networks, and green communication and networks.
Supported by:
The work of YAN Jintao, CHEN Tan, XIE Bowen, ZHOU Sheng and NIU Zhisheng are sponsored in part by the National Key R&D Program of China(2020YFB1806605);the National Natural Science Foundation of China(62022049);OPPO. The work of SUN Yuxuan is supported by the Fundamental Research Funds for the Central Universities(2022JBXT001)

摘要/Abstract

Abstract:

Federated learning (FL) is a distributed machine learning (ML) framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data. In FL, the limited number of participants for model aggregation and communication latency are two major bottlenecks. Hierarchical federated learning (HFL), with a cloud-edge-client hierarchy, can leverage the large coverage of cloud servers and the low transmission latency of edge servers. There are growing research interests in implementing FL in vehicular networks due to the requirements of timely ML training for intelligent vehicles. However, the limited number of participants in vehicular networks and vehicle mobility degrade the performance of FL training. In this context, HFL, which stands out for lower latency, wider coverage and more participants, is promising in vehicular networks. In this paper, we begin with the background and motivation of HFL and the feasibility of implementing HFL in vehicular networks. Then, the architecture of HFL is illustrated. Next, we clarify new issues in HFL and review several existing solutions. Furthermore, we introduce some typical use cases in vehicular networks as well as our initial efforts on implementing HFL in vehicular networks. Finally, we conclude with future research directions.

Key words: hierarchical federated learning, vehicular network, mobility, convergence analysis

. [J]. ZTE Communications, 2023, 21(1): 38-45.

YAN Jintao, CHEN Tan, XIE Bowen, SUN Yuxuan, ZHOU Sheng, NIU Zhisheng. Hierarchical Federated Learning: Architecture, Challenges, and Its Implementation in Vehicular Networks[J]. ZTE Communications, 2023, 21(1): 38-45.

图/表 7

参考文献 34

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Highlight	Reference
A comprehensive survey of FL in wireless networks	Ref. [1]
A tutorial on timely edge learning, aiming to minimize the communication and computation latency in FL training	Ref. [2]
A comprehensive survey of FL and MEC	Ref. [11]
A tutorial on the implementation of FL in vehicular networks and the major challenges of learning and communications	Ref. [12]

Highlight	Reference
A comprehensive survey of FL in wireless networks	Ref. [1]
A tutorial on timely edge learning, aiming to minimize the communication and computation latency in FL training	Ref. [2]
A comprehensive survey of FL and MEC	Ref. [11]
A tutorial on the implementation of FL in vehicular networks and the major challenges of learning and communications	Ref. [12]

Category	Highlight	Reference
Convergence analysis	Effect of edge and cloud aggregation intervals and local update step size with both convex and non-convex loss functions	Ref. [5]
	Extending Ref. [5] into HFL with quantization and carrying out the convergence analysis	Ref. [13]
	Effect of local iterations, edge epochs, global epochs, network topology and node heterogeneity on the convergence performance for a graph-based edge topology	Ref. [14]
	Extending Ref. [14] into HFL with data heterogeneity, random grouping and multi-layer architecture	Ref. [15]
	Mobility-aware HFL	Ref. [16]
Resource management	Joint resource allocation and edge association	Refs. [17–19]
	Joint resource allocation and interval control	Ref. [20]
	Joint resource allocation and incentive mechanism design	Ref. [21]
Other practical considerations	Multi-stage HFL with device-to-device communications	Ref. [22]

Category	Highlight	Reference
Convergence analysis	Effect of edge and cloud aggregation intervals and local update step size with both convex and non-convex loss functions	Ref. [5]
	Extending Ref. [5] into HFL with quantization and carrying out the convergence analysis	Ref. [13]
	Effect of local iterations, edge epochs, global epochs, network topology and node heterogeneity on the convergence performance for a graph-based edge topology	Ref. [14]
	Extending Ref. [14] into HFL with data heterogeneity, random grouping and multi-layer architecture	Ref. [15]
	Mobility-aware HFL	Ref. [16]
Resource management	Joint resource allocation and edge association	Refs. [17–19]
	Joint resource allocation and interval control	Ref. [20]
	Joint resource allocation and incentive mechanism design	Ref. [21]
Other practical considerations	Multi-stage HFL with device-to-device communications	Ref. [22]

Hierarchical Federated Learning: Architecture, Challenges, and Its Implementation in Vehicular Networks

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