Asynchronous Federated Learning via Blockchain in Edge Computing Networks

HUANG Xiaoge; DENG Xuesong; CHEN Qianbin; ZHANG Jie

doi:10.11999/JEIT221517

Volume 46 Issue 1

Jan. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(1): 195-203

HUANG Xiaoge, DENG Xuesong, CHEN Qianbin, ZHANG Jie. Asynchronous Federated Learning via Blockchain in Edge Computing Networks[J]. Journal of Electronics & Information Technology, 2024, 46(1): 195-203. doi: 10.11999/JEIT221517

Citation:

HUANG Xiaoge, DENG Xuesong, CHEN Qianbin, ZHANG Jie. Asynchronous Federated Learning via Blockchain in Edge Computing Networks[J]. Journal of Electronics & Information Technology, 2024, 46(1): 195-203. doi: 10.11999/JEIT221517

Citation:

PDF( 3666 KB)

Asynchronous Federated Learning via Blockchain in Edge Computing Networks

doi: 10.11999/JEIT221517

College of Communication and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61831002), Innovation Project of the Common Key Technology of Chongqing Science and Technology Industry (cstc2018jcyjAx0383)

Received Date: 2022-12-06
Rev Recd Date: 2023-05-17

Available Online: 2023-05-24

Publish Date: 2024-01-17

Abstract

Abstract

Because of the information explosion caused by the surge of data, traditional centralized cloud computing is overwhelmed, Edge Computing Network (ECN) is proposed to alleviate the burden on cloud servers. In contrast, by permitting Federated Learning (FL) in the ECN, data localization processing could be realized to successfully address the data security problem of Edge Nodes (ENs) in collaborative learning. However, traditional FL exposes the central server to single-point attacks, resulting in system performance degradation or even task failure. In this paper, we propose Asynchronous Federated Learning based on Blockchain technology (AFLChain) in the ECN that can dynamically assign learning tasks to ENs based on their computing capabilities to boost learning efficiency. In addition, based on the computing capability of ENs, model training progress and historical reputation, the entropy weight reputation mechanism is implemented to assess and rank the enthusiasm of ENs, eliminating low quality ENs to further improve the performance of the AFLChain. Finally, the Subgradient based Optimal Resource Allocation (SORA) algorithm is proposed to reduce network latency by optimizing transmission power and computing resource allocation simultaneously. The simulation results demonstrate the model training efficiency of the AFLChain and the convergence of the SORA algorithm and the efficacy of the proposed algorithms.
- Asynchronous federated learning,
- Blockchain,
- Resource allocation,
- Edge Computing Network (ECN)

FullText(HTML)

References(15)

References

[1]	ZHANG Jing and TAO Dacheng. Empowering things with intelligence: A survey of the progress, challenges, and opportunities in artificial intelligence of things[J]. IEEE Internet of Things Journal, 2021, 8(10): 7789–7817. doi: 10.1109/JIOT.2020.3039359
[2]	JIANG Chunxiao, ZHANG Haijun, REN Yong, et al. Machine learning paradigms for next-generation wireless networks[J]. IEEE Wireless Communications, 2017, 24(2): 98–105. doi: 10.1109/MWC.2016.1500356WC
[3]	LIM W Y B, LUONG N C, HOANG D T, et al. Federated learning in mobile edge networks: A comprehensive survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(3): 2031–2063. doi: 10.1109/COMST.2020.2986024
[4]	LI Tian, SAHU A K, TALWALKAR A, et al. Federated learning: Challenges, methods, and future directions[J]. IEEE Signal Processing Magazine, 2020, 37(3): 50–60. doi: 10.1109/MSP.2020.2975749
[5]	IEEE Std 3652.1-2020 IEEE guide for architectural framework and application of federated machine learning[S]. IEEE, 2021.
[6]	SHEN Xin, LI Zhuo, and CHEN Xin. Node selection strategy design based on reputation mechanism for hierarchical federated learning[C]. 2022 18th International Conference on Mobility, Sensing and Networking (MSN), Guangzhou, China, 2022: 718–722.
[7]	LIU Jianchun, XU Hongli, WANG Lun, et al. Adaptive asynchronous federated learning in resource-constrained edge computing[J]. IEEE Transactions on Mobile Computing, 2023, 22(2): 674–690. doi: 10.1109/TMC.2021.3096846
[8]	LI Zonghang, ZHOU Huaman, ZHOU Tianyao, et al. ESync: Accelerating intra-domain federated learning in heterogeneous data centers[J]. IEEE Transactions on Services Computing, 2022, 15(4): 2261–2274. doi: 10.1109/TSC.2020.3044043
[9]	CHEN Yang, SUN Xiaoyan, and JIN Yaochu. Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(10): 4229–4238. doi: 10.1109/TNNLS.2019.2953131
[10]	CAO Mingrui, ZHANG Long, and CAO Bin. Toward on-device federated learning: A direct acyclic graph-based blockchain approach[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(4): 2028–2042. doi: 10.1109/TNNLS.2021.3105810
[11]	FENG Lei, YANG Zhixiang, GUO Shaoyong, et al. Two-layered blockchain architecture for federated learning over the mobile edge network[J]. IEEE Network, 2022, 36(1): 45–51. doi: 10.1109/MNET.011.2000339
[12]	QIN Zhenquan, YE Jin, MENG Jie, et al. Privacy-preserving blockchain-based federated learning for marine internet of things[J]. IEEE Transactions on Computational Social Systems, 2022, 9(1): 159–173. doi: 10.1109/TCSS.2021.3100258
[13]	XU Chenhao, QU Youyang, LUAN T H, et al. An efficient and reliable asynchronous federated learning scheme for smart public transportation[J]. IEEE Transactions on Vehicular Technology, 2023, 72(5): 6584–6598. doi: 10.1109/TVT.2022.3232603
[14]	LI Qinbin, HE Bingsheng, and SONG D. Model-contrastive federated learning[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, 2021: 10708–10717.
[15]	范盛金. 一元三次方程的新求根公式与新判别法[J]. 海南师范学院学报(自然科学版), 1989, 2(2): 91–98. FAN Shengjin. A new extracting formula and a new distinguishing means on the one variable cubic equation[J]. Natural Science Journal of Hainan Normal College, 1989, 2(2): 91–98.