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

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