Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach

Lingling Zhang; Yanxiang Jiang; Fu Chun Zheng; Mehdi Bennis; Xiaohu You

doi:10.1109/ICCWorkshops53468.2022.9814649

Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach

Lingling Zhang
, Yanxiang Jiang^*
, Fu Chun Zheng
, Mehdi Bennis
, Xiaohu You

^*Corresponding author for this work

Southeast University, Nanjing
Harbin Institute of Technology Shenzhen
University of Oulu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

Abstract

The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an efficient task offloading scheme. In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs. To solve the problem, a federated deep reinforcement learning (DRL) based algorithm is proposed, where the deep deterministic policy gradient (DDPG) algorithm performs computation offloading and resource allocation in each F-AP. Federated learning is exploited to train the DDPG agents in order to decrease the computing complexity of training process and protect the user privacy. Simulation results show that the proposed federated DDPG algorithm can achieve lower task execution delay and energy consumption of MDs more quickly compared with the other existing strategies.

Original language	English
Title of host publication	2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	97-102
Number of pages	6
ISBN (Electronic)	9781665426718
DOIs	https://doi.org/10.1109/ICCWorkshops53468.2022.9814649
State	Published - 2022
Externally published	Yes
Event	2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022 - Seoul, Korea, Republic of Duration: 16 May 2022 → 20 May 2022

Publication series

Name	2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

Conference

Conference	2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
Country/Territory	Korea, Republic of
City	Seoul
Period	16/05/22 → 20/05/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Fog radio access networks (F-RANs)
computation offloading
deep deterministic policy gradient (DDPG)
federated learning
resource allocation

Access to Document

10.1109/ICCWorkshops53468.2022.9814649

Cite this

Zhang, L., Jiang, Y., Zheng, F. C., Bennis, M., & You, X. (2022). Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach. In 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022 (pp. 97-102). (2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCWorkshops53468.2022.9814649

Zhang, Lingling ; Jiang, Yanxiang ; Zheng, Fu Chun et al. / Computation Offloading and Resource Allocation in F-RANs : A Federated Deep Reinforcement Learning Approach. 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 97-102 (2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022).

@inproceedings{a3ee5932f7b447aba4eff119792e64c1,

title = "Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach",

abstract = "The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an efficient task offloading scheme. In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs. To solve the problem, a federated deep reinforcement learning (DRL) based algorithm is proposed, where the deep deterministic policy gradient (DDPG) algorithm performs computation offloading and resource allocation in each F-AP. Federated learning is exploited to train the DDPG agents in order to decrease the computing complexity of training process and protect the user privacy. Simulation results show that the proposed federated DDPG algorithm can achieve lower task execution delay and energy consumption of MDs more quickly compared with the other existing strategies.",

keywords = "Fog radio access networks (F-RANs), computation offloading, deep deterministic policy gradient (DDPG), federated learning, resource allocation",

author = "Lingling Zhang and Yanxiang Jiang and Zheng, \{Fu Chun\} and Mehdi Bennis and Xiaohu You",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022 ; Conference date: 16-05-2022 Through 20-05-2022",

year = "2022",

doi = "10.1109/ICCWorkshops53468.2022.9814649",

language = "英语",

series = "2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "97--102",

booktitle = "2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022",

address = "美国",

}

Zhang, L, Jiang, Y, Zheng, FC, Bennis, M & You, X 2022, Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach. in 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022. 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022, Institute of Electrical and Electronics Engineers Inc., pp. 97-102, 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022, Seoul, Korea, Republic of, 16/05/22. https://doi.org/10.1109/ICCWorkshops53468.2022.9814649

Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach. / Zhang, Lingling; Jiang, Yanxiang; Zheng, Fu Chun et al.
2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 97-102 (2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Computation Offloading and Resource Allocation in F-RANs

T2 - 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

AU - Zhang, Lingling

AU - Jiang, Yanxiang

AU - Zheng, Fu Chun

AU - Bennis, Mehdi

AU - You, Xiaohu

PY - 2022

Y1 - 2022

N2 - The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an efficient task offloading scheme. In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs. To solve the problem, a federated deep reinforcement learning (DRL) based algorithm is proposed, where the deep deterministic policy gradient (DDPG) algorithm performs computation offloading and resource allocation in each F-AP. Federated learning is exploited to train the DDPG agents in order to decrease the computing complexity of training process and protect the user privacy. Simulation results show that the proposed federated DDPG algorithm can achieve lower task execution delay and energy consumption of MDs more quickly compared with the other existing strategies.

AB - The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an efficient task offloading scheme. In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs. To solve the problem, a federated deep reinforcement learning (DRL) based algorithm is proposed, where the deep deterministic policy gradient (DDPG) algorithm performs computation offloading and resource allocation in each F-AP. Federated learning is exploited to train the DDPG agents in order to decrease the computing complexity of training process and protect the user privacy. Simulation results show that the proposed federated DDPG algorithm can achieve lower task execution delay and energy consumption of MDs more quickly compared with the other existing strategies.

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KW - deep deterministic policy gradient (DDPG)

KW - federated learning

KW - resource allocation

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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 16 May 2022 through 20 May 2022

ER -

Zhang L, Jiang Y, Zheng FC, Bennis M, You X. Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach. In 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 97-102. (2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022). doi: 10.1109/ICCWorkshops53468.2022.9814649

Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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