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

[1] Blockchain-Enabled Computing Resource Trading: A Deep Reinforcement Learning Approach
Xie, Zixuan
Wu, Run
Hu, Miao
Tian, Haibo
2020 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), 2020,
[2] Blockchain-enabled Federated Learning: A Survey
Qu, Youyang
Uddin, Md Palash
Gan, Chenquan
Xiang, Yong
Gao, Longxiang
Yearwood, John
ACM COMPUTING SURVEYS, 2023, 55 (04)
[3] A Blockchain-Enabled Demand Management and Control Framework Driven by Deep Reinforcement Learning
Ma, Rui
Yi, Zhehan
Xiang, Yingmeng
Shi, Di
Xu, Chunlei
Wu, Haiwei
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2023, 70 (01) : 430 - 440
[4] On the Decentralization of Blockchain-enabled Asynchronous Federated Learning
Wilhelmi, Francesc
Guerra, Elia
Dini, Paolo
2023 IEEE 9TH INTERNATIONAL CONFERENCE ON NETWORK SOFTWARIZATION, NETSOFT, 2023, : 408 - 413
[5] Blockchain-Enabled Federated Learning With Mechanism Design
Toyoda, Kentaroh
Zhao, Jun
Zhang, Allan Neng Sheng
Mathiopoulos, P. Takis
IEEE ACCESS, 2020, 8 : 219744 - 219756
[6] AFLChain: Blockchain-enabled Asynchronous Federated Learning in Edge Computing Network
Huang, Xiaoge
Deng, Xuesong
Chen, Qianbin
Zhang, Jie
2023 IEEE 97TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-SPRING, 2023,
[7] Deep Reinforcement Learning for Blockchain-Enabled Mobile Edge Computing Systems
Li, Jie
Feng, Jie
Pei, Qingqi
Du, Jianbo
2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2020,
[8] Deep Reinforcement Learning for Resource Allocation in Blockchain-based Federated Learning
Dai, Yueyue
Yang, Huijiong
Yang, Huiran
ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2023, : 179 - 184
[9] Towards blockchain-enabled decentralized and secure federated learning
Ma, Xuyang
Xu, Du
Wolter, Katinka
INFORMATION SCIENCES, 2024, 665
[10] Blockchain-enabled Federated Learning: Models, Methods and Applications
Li C.
Yuan Y.
Zheng Z.-Y.
Yang D.
Wang F.-Y.
Zidonghua Xuebao/Acta Automatica Sinica, 2024, 50 (06): : 1059 - 1085

← 1 2 3 4 5 →