標題: Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach
作者: Tran The Anh
Nguyen Cong Luong
Niyato, Dusit
Kim, Dong In
Wang, Li-Chun
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: Mobile crowd;federated learning;deep reinforcement learning
公開日期: 1-十月-2019
摘要: In this letter, we consider the concept of mobile crowd-machine learning (MCML) for a federated learning model. The MCML enables mobile devices in a mobile network to collaboratively train neural network models required by a server while keeping data on the mobile devices. The MCML thus addresses data privacy issues of traditional machine learning. However, the mobile devices are constrained by energy, CPU, and wireless bandwidth. Thus, to minimize the energy consumption, training time, and communication cost, the server needs to determine proper amounts of data and energy that the mobile devices use for training. However, under the dynamics and uncertainty of the mobile environment, it is challenging for the server to determine the optimal decisions on mobile device resource management. In this letter, we propose to adopt a deep Q-learning algorithm that allows the server to learn and find optimal decisions without any a priori knowledge of network dynamics. Simulation results show that the proposed algorithm outperforms the static algorithms in terms of energy consumption and training latency.
URI: http://dx.doi.org/10.1109/LWC.2019.2917133
http://hdl.handle.net/11536/153559
ISSN: 2162-2337
DOI: 10.1109/LWC.2019.2917133
期刊: IEEE WIRELESS COMMUNICATIONS LETTERS
Volume: 8
Issue: 5
起始頁: 1345
結束頁: 1348
顯示於類別:期刊論文