Semi-Asynchronous Hierarchical Federated Learning over Mobile Edge Networks

Mobile edge network has been recognized as a promising technology for future wireless communications. However, mobile edge networks usually gathering large amounts of data, which makes it difficult to explore data science efficiently. Currently, federated learning has been proposed as an appealing a...

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Bibliographic Details
Published in:IEEE access Vol. 11; p. 1
Main Authors: Chen, Qimei, You, Zehua, Wu, Jing, Liu, Yunpeng, Jiang, Hao
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computational modeling
Data models
Data science
Edge association
Edge computing
Federated learning
Mixed integer
mobile edge network
Network latency
Nonlinear programming
Resource allocation
Resource management
semi-asynchronous
Servers
Wireless communication
Wireless communications
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Mobile edge network has been recognized as a promising technology for future wireless communications. However, mobile edge networks usually gathering large amounts of data, which makes it difficult to explore data science efficiently. Currently, federated learning has been proposed as an appealing approach to allow users to cooperatively reap the benefits from trained participants. In this paper, we propose a novel Semi-Asynchronous Hierarchical Federated Learning (SAHFL) framework for mobile edge networks that enables elastic edge to cloud model aggregation from data sensing. We further formulate a joint edge node association and resource allocation problem under the proposed SAHFL framework to prevent personalities of heterogeneous devices and achieve communication-efficiency. To deal with our proposed Mixed integer nonlinear programming (MINLP) problem, we introduce a distributed Alternating Direction Method of Multipliers (ADMM)- Block Coordinate Update (BCU) algorithm. With this algorithm, a tradeoff between training accuracy and transmission latency has been derived. Numerical results demonstrate the advantages of the proposed algorithm in terms of training overhead and model performance.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3227561