Federated learning based caching in fog computing for future smart cities

被引:2
作者
Sharma, Sushant [1 ]
Gupta, Nitin [1 ]
机构
[1] Natl Inst Technol, Dept Comp Sci & Engn, Hamirpur, Himachal Prades, India
关键词
caching; cloud; edge computing; federated learning; fog computing; machine learning;
D O I
10.1002/itl2.225
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Edge devices in the Internet of Things (IoT) networks are responsible for the continuous generation and communication of massive chunks of transient data to the fog devices for data processing. However, resources at the fog nodes are limited. Therefore, objective of the work is efficient caching, which is achieved using Federated Learning (FL), where the data used for learning is not gathered centrally but remains where it is produced. Therefore, the heavy transmission of the data to the fog nodes for learning is not required. Simulation results depict the advantages of using this approach over other centralized approaches.
引用
收藏
页数:6
相关论文
共 8 条
[1]  
[Anonymous], 2016, 2016 IEEEC INT C
[2]   Dueling Deep-Q-Network Based Delay-Aware Cache Update Policy for Mobile Users in Fog Radio Access Networks [J].
Guo, Boren ;
Zhang, Xin ;
Sheng, Qiwei ;
Yang, Hongwen .
IEEE ACCESS, 2020, 8 :7131-7141
[3]  
Lee G, 2017, 2017 IEEE FOG WORLD CONGRESS (FWC), P85
[4]  
Li T., 2019, CHALLENGES
[5]   Federated Learning in Mobile Edge Networks: A Comprehensive Survey [J].
Lim, Wei Yang Bryan ;
Nguyen Cong Luong ;
Dinh Thai Hoang ;
Jiao, Yutao ;
Liang, Ying-Chang ;
Yang, Qiang ;
Niyato, Dusit ;
Miao, Chunyan .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2020, 22 (03) :2031-2063
[6]  
Wang Y, 2016, IEEE INT SYMP CIRC S, P1, DOI 10.1109/ISCAS.2016.7527155
[7]  
Yu ZX, 2018, IEEE GLOB COMM CONF
[8]   Caching Transient Data for Internet of Things: A Deep Reinforcement Learning Approach [J].
Zhu, Hao ;
Cao, Yang ;
Wei, Xiao ;
Wang, Wei ;
Jiang, Tao ;
Jin, Shi .
IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (02) :2074-2083