Communication-Efficient Federated Edge Learning for NR-U-Based IIoT Networks

Chen, Qimei; Xu, Xiaoxia; You, Zehua; Jiang, Hao; Wang, Fei‐Yue

doi:10.1109/jiot.2021.3136205

Cited by 12 publications

(3 citation statements)

References 24 publications

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“…Since the edge model undergoes an edge elastic update process when it broadcasts to devices, we have the following edge model update according to (18), (19), (20), as…”

Section: Appendix a Proof Of Theoremmentioning

confidence: 99%

“…In [17], the authors proposed a joint device association and wireless resource allocation scheme under IID and non-IID datasets, respectively. The authors in [18] proposed a novel device selection and resource allocation scheme under wireless resource fruitful unlicensed spectrum (NR-U) networks. However, in this case, the computing resources of those unselected devices are wasted.…”

Section: Introductionmentioning

confidence: 99%

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Semi-Asynchronous Hierarchical Federated Learning Over Mobile Edge Networks

Chen

You

et al. 2023

IEEE Access

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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.INDEX TERMS Edge association, federated learning, mobile edge network, resource allocation, semiasynchronous.

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“…Since the edge model undergoes an edge elastic update process when it broadcasts to devices, we have the following edge model update according to (18), (19), (20), as…”

Section: Appendix a Proof Of Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semi-Asynchronous Hierarchical Federated Learning Over Mobile Edge Networks

Chen

You

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…With the rapid development of advanced industrial manufacturing modes such as Industry 4.0, intelligent factory, and flexible manufacturing, communication technology and the manufacturing industry have become deeply integrated in recent years. Consequently, the digital and integrated industrial Internet of Things (IIoT) has emerged as a research hotspot [1][2][3][4][5]. However, the advantages of the three major application scenarios of 5G are incompatible and cannot coexist simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Task-Offloading and Resource Allocation Strategy in Multidomain Cooperation for IIoT

et al. 2023

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This study proposes a task-offloading and resource allocation strategy in multidomain cooperation (TARMC) for the industrial Internet of Things (IIoT) to resolve the problem of the non-uniform distribution of task computation among various cluster domain networks in the IIoT and the solidification of traditional industrial wireless network architecture, which produces low efficiency of static resource allocation and high delay in closed-loop data processing. Based on the closed-loop process of task interaction of intelligent terminals in wireless networks, the proposed strategy constructs a network model of multidomain collaborative task-offloading and resource allocation in IIoT for flexible and dynamic resource allocation among intelligent terminals, edge servers, and cluster networks. Considering the partial offloading mechanism, various tasks were segmented into multiple subtasks marked at bit-level per demand, which enabled local and edge servers to process all subtasks in parallel. Moreover, this study established a utility function for the closed-loop delay and terminal energy consumption of task processing, which transformed the process of multidomain collaborative task-offloading and resource allocation into the problem of task computing revenue. Furthermore, an improved Cuckoo Search algorithm was developed to derive the optimal offloading position and resource allocation decision through an alternating iterative method. The simulation results revealed that TARMC performed better than strategies.

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