Deep Reinforcement Learning for Resource Management in Blockchain-Enabled Federated Learning Network

被引：11

作者：

Hieu, Nguyen Quang ^{[1
]}

Tran, The Anh ^{[2
]}

Nguyen, Cong Luong ^{[3
]}

Niyato, Dusit ^{[2
]}

Kim, Dong In ^{[4
]}

Elmroth, Erik ^{[5
]}

机构：

[1] University of Technology Sydney, School of Electrical and Data Engineering, Sydney,NSW,2007, Australia

[2] Nanyang Technological University, School of Computer Science and Engineering, Jurong West, Singapore

[3] Phenikaa University, Faculty of Computer Science, Hanoi,10000, Viet Nam

[4] Sungkyunkwan University, Department of Electrical and Computer Engineering, Seoul,16419, Korea, Republic of

[5] Umeå University, Department of Computing Science, Umeå,901 87, Sweden

来源：

IEEE Networking Letters | 2022年 / 4卷 / 03期

关键词：

Deep learning - Queueing theory - Reinforcement learning - Blockchain - Resource allocation;

D O I：

10.1109/LNET.2022.3173971

中图分类号：

学科分类号：

摘要：

Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in blockchain in a reliable manner. However, one problem is the increase of the training latency due to the mining process. Moreover, mobile devices have energy and CPU constraints. Therefore, the machine learning model owner (MLMO) needs to decide the data and energy that the mobile devices use for the training and determine the block generation rate to minimize the system latency and mining cost while achieving the target accuracy. Under the uncertainty of BFL, we propose to use deep reinforcement learning to find the optimal decisions for the MLMO. © 2019 IEEE.

引用

页码：137 / 141

共 50 条

[21] Deep reinforcement learning approach for computation offloading in blockchain-enabled communications systems
Alam, Tanweer
Ullah, Arif
Benaida, Mohamed
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2022, 14 (8) : 9959 - 9972
[22] Blockchain-Enabled Deep Reinforcement Learning Approach for Performance Optimization on the Internet of Things
Alam, Tanweer
WIRELESS PERSONAL COMMUNICATIONS, 2022, 126 (02) : 995 - 1011
[23] Blockchain-Enabled Federated Learning with Differential Privacy for Internet of Vehicles
Cui, Chi
Du, Haiping
Jia, Zhijuan
He, Yuchu
Wang, Lipeng
CMC-COMPUTERS MATERIALS & CONTINUA, 2024, 81 (01): : 1581 - 1593
[24] Hierarchical Service Composition via Blockchain-enabled Federated Learning
Huang, Li
Zhao, Lu
Liu, Yansong
Zhao, Yao
DATA SCIENCE AND ENGINEERING, 2024, 9 (04) : 449 - 463
[25] A scalable blockchain-enabled federated learning architecture for edge computing
Ren, Shuyang
Kim, Eunsam
Lee, Choonhwa
PLOS ONE, 2024, 19 (08):
[26] Efficient Information Dissemination in Blockchain-Enabled Federated Learning for IoV
Ghimire, Bimal
Rawat, Danda B.
Rahman, Abdul
IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (09): : 15310 - 15319
[27] Blockchain-Enabled Secure Federated Learning for Digital Twin Networks
Cai, Lingyi
Hu, Qiwei
Jiang, Tao
Niyato, Dusit
IEEE WIRELESS COMMUNICATIONS, 2024,
[28] Blockchain-Enabled Clustered Federated Learning in Fog Computing Networks
Huang, Xiaoge
Zhi, Chen
Chen, Qianbin
Zhang, Jie
2021 IEEE 94TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-FALL), 2021,
[29] Auditable and Verifiable Federated Learning Based on Blockchain-Enabled Decentralization
Kalapaaking, Aditya Pribadi
Khalil, Ibrahim
Yi, Xun
Lam, Kwok-Yan
Huang, Guang-Bin
Wang, Ning
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2025, 36 (01) : 102 - 115
[30] Distance-Aware Hierarchical Federated Learning in Blockchain-Enabled Edge Computing Network
Huang, Xiaoge
Wu, Yuhang
Liang, Chengchao
Chen, Qianbin
Zhang, Jie
IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (21) : 19163 - 19176

← 1 2 3 4 5 →