Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs
With the rapid development of mobile communication and the sharp increase of smart mobile devices, wireless data traffic has experienced explosive growth in recent years, thus injecting tremendous traffic into the network. Fog Radio Access Network (F-RAN) is a promising wireless network architecture...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/1/215 |
| _version_ | 1797542845888856064 |
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| author | Tuo Xiao Taiping Cui S. M. Riazul Islam Qianbin Chen |
| author_facet | Tuo Xiao Taiping Cui S. M. Riazul Islam Qianbin Chen |
| author_sort | Tuo Xiao |
| collection | DOAJ |
| description | With the rapid development of mobile communication and the sharp increase of smart mobile devices, wireless data traffic has experienced explosive growth in recent years, thus injecting tremendous traffic into the network. Fog Radio Access Network (F-RAN) is a promising wireless network architecture to accommodate the fast growing data traffic and improve the performance of network service. By deploying content caching in F-RAN, fast and repeatable data access can be achieved, which reduces network traffic and transmission latency. Due to the capacity limit of caches, it is essential to predict the popularity of the content and pre-cache them in edge nodes. In general, the classic prediction approaches require the gathering of users’ personal information at a central unit, giving rise to users’ privacy issues. In this paper, we propose an intelligent F-RANs framework based on federated learning (FL), which does not require gathering user data centrally on the server for training, so it can effectively ensure the privacy of users. In the work, federated learning is applied to user demand prediction, which can accurately predict the content popularity distribution in the network. In addition, to minimize the total traffic cost of the network in consideration of user content requests, we address the allocation of storage resources and content placement in the network as an integrated model and formulate it as an Integer Linear Programming (ILP) problem. Due to the high computational complexity of the ILP problem, two heuristic algorithms are designed to solve it. Simulation results show that the performance of our proposed algorithm is close to the optimal solution. |
| first_indexed | 2024-03-10T13:36:16Z |
| format | Article |
| id | doaj.art-d8e53834dfb0486387f99dd5593b1545 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:36:16Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d8e53834dfb0486387f99dd5593b15452023-11-21T07:29:46ZengMDPI AGSensors1424-82202020-12-0121121510.3390/s21010215Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANsTuo Xiao0Taiping Cui1S. M. Riazul Islam2Qianbin Chen3School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Nan-An District, Chongqing 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Nan-An District, Chongqing 400065, ChinaDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, KoreaChongqing Key Labs of Mobile Communications, Chongqing 400065, ChinaWith the rapid development of mobile communication and the sharp increase of smart mobile devices, wireless data traffic has experienced explosive growth in recent years, thus injecting tremendous traffic into the network. Fog Radio Access Network (F-RAN) is a promising wireless network architecture to accommodate the fast growing data traffic and improve the performance of network service. By deploying content caching in F-RAN, fast and repeatable data access can be achieved, which reduces network traffic and transmission latency. Due to the capacity limit of caches, it is essential to predict the popularity of the content and pre-cache them in edge nodes. In general, the classic prediction approaches require the gathering of users’ personal information at a central unit, giving rise to users’ privacy issues. In this paper, we propose an intelligent F-RANs framework based on federated learning (FL), which does not require gathering user data centrally on the server for training, so it can effectively ensure the privacy of users. In the work, federated learning is applied to user demand prediction, which can accurately predict the content popularity distribution in the network. In addition, to minimize the total traffic cost of the network in consideration of user content requests, we address the allocation of storage resources and content placement in the network as an integrated model and formulate it as an Integer Linear Programming (ILP) problem. Due to the high computational complexity of the ILP problem, two heuristic algorithms are designed to solve it. Simulation results show that the performance of our proposed algorithm is close to the optimal solution.https://www.mdpi.com/1424-8220/21/1/215fog radio access networkcontent placementstorage allocationfederated learning |
| spellingShingle | Tuo Xiao Taiping Cui S. M. Riazul Islam Qianbin Chen Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs Sensors fog radio access network content placement storage allocation federated learning |
| title | Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs |
| title_full | Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs |
| title_fullStr | Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs |
| title_full_unstemmed | Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs |
| title_short | Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs |
| title_sort | joint content placement and storage allocation based on federated learning in f rans |
| topic | fog radio access network content placement storage allocation federated learning |
| url | https://www.mdpi.com/1424-8220/21/1/215 |
| work_keys_str_mv | AT tuoxiao jointcontentplacementandstorageallocationbasedonfederatedlearninginfrans AT taipingcui jointcontentplacementandstorageallocationbasedonfederatedlearninginfrans AT smriazulislam jointcontentplacementandstorageallocationbasedonfederatedlearninginfrans AT qianbinchen jointcontentplacementandstorageallocationbasedonfederatedlearninginfrans |