Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network
Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/13/4738 |
| _version_ | 1797442004829863936 |
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| author | Xing Chen Guizhong Liu |
| author_facet | Xing Chen Guizhong Liu |
| author_sort | Xing Chen |
| collection | DOAJ |
| description | Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices. |
| first_indexed | 2024-03-09T12:35:32Z |
| format | Article |
| id | doaj.art-0a20d192afaf4db1a7c4ebe5ae6698ff |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:35:32Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0a20d192afaf4db1a7c4ebe5ae6698ff2023-11-30T22:24:47ZengMDPI AGSensors1424-82202022-06-012213473810.3390/s22134738Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge NetworkXing Chen0Guizhong Liu1School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaMobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices.https://www.mdpi.com/1424-8220/22/13/4738smart citymobile edge computingtask offloadingresource allocationDDPGfederated learning |
| spellingShingle | Xing Chen Guizhong Liu Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network Sensors smart city mobile edge computing task offloading resource allocation DDPG federated learning |
| title | Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network |
| title_full | Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network |
| title_fullStr | Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network |
| title_full_unstemmed | Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network |
| title_short | Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network |
| title_sort | federated deep reinforcement learning based task offloading and resource allocation for smart cities in a mobile edge network |
| topic | smart city mobile edge computing task offloading resource allocation DDPG federated learning |
| url | https://www.mdpi.com/1424-8220/22/13/4738 |
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