research-article

PSDF: Privacy-aware IoV Service Deployment with Federated Learning in Cloud-Edge Computing

Authors:

Md Zakirul Alam BhuiyanAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 13, Issue 5

Article No.: 70, Pages 1 - 22

https://doi.org/10.1145/3501810

Published: 13 October 2022 Publication History

Abstract

Through the collaboration of cloud and edge, cloud-edge computing allows the edge that approximates end-users undertakes those non-computationally intensive service processing of the cloud, reducing the communication overhead and satisfying the low latency requirement of Internet of Vehicle (IoV). With cloud-edge computing, the computing tasks in IoV is able to be delivered to the edge servers (ESs) instead of the cloud and rely on the deployed services of ESs for a series of processing. Due to the storage and computing resource limits of ESs, how to dynamically deploy partial services to the edge is still a puzzle. Moreover, the decision of service deployment often requires the transmission of local service requests from ESs to the cloud, which increases the risk of privacy leakage. In this article, a method for privacy-aware IoV service deployment with federated learning in cloud-edge computing, named PSDF, is proposed. Technically, federated learning secures the distributed training of deployment decision network on each ES by the exchange and aggregation of model weights, avoiding the original data transmission. Meanwhile, homomorphic encryption is adopted for the uploaded weights before the model aggregation on the cloud. Besides, a service deployment scheme based on deep deterministic policy gradient is proposed. Eventually, the performance of PSDF is evaluated by massive experiments.

References

[1]

Abbas Acar, Hidayet Aksu, A. Selcuk Uluagac, and Mauro Conti. 2018. A survey on homomorphic encryption schemes: Theory and implementation. ACM Comput. Surv. 51, 4 (2018), 1–35.

Digital Library

[2]

Paul F. Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep reinforcement learning from human preferences. In Advances in Neural Information Processing Systems, Vol. 30.

[3]

Anne G. E. Collins and Jeffrey Cockburn. 2020. Beyond dichotomies in reinforcement learning. Nat. Rev. Neurosci. 21, 10 (2020), 576–586.

[4]

Yueyue Dai, Du Xu, Sabita Maharjan, Guanhua Qiao, and Yan Zhang. 2019. Artificial intelligence empowered edge computing and caching for internet of vehicles. IEEE Wireless Commun. 26, 3 (2019), 12–18.

Digital Library

[5]

Shuiguang Deng, Zhengzhe Xiang, Javid Taheri, Khoshkholghi Ali Mohammad, Jianwei Yin, Albert Zomaya, and Schahram Dustdar. 2020. Optimal application deployment in resource constrained distributed edges. IEEE Trans. Mobile Comput. 20, 5 (2020), 1907–1923.

[6]

Scott Fujimoto, David Meger, and Doina Precup. 2019. Off-policy deep reinforcement learning without exploration. In Proceedings of the International Conference on Machine Learning. PMLR, 2052–2062.

[7]

Ting He, Hana Khamfroush, Shiqiang Wang, Tom La Porta, and Sebastian Stein. 2018. It’s hard to share: Joint service placement and request scheduling in edge clouds with sharable and non-sharable resources. In Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS’18). IEEE, 365–375.

[8]

Xiangwang Hou, Zhiyuan Ren, Jingjing Wang, Wenchi Cheng, Yong Ren, Kwang-Cheng Chen, and Hailin Zhang. 2020. Reliable computation offloading for edge computing-enabled software-defined IoV. IEEE IoT J. 7, 8 (2020), 7097–7111.

[9]

Jonathan Kaltz and Yehuda Lindell. 2008. Introduction to modern cryptography: Principles and protocols. Chapman and Hall.

[10]

Nguyen Cong Luong, Dinh Thai Hoang, Shimin Gong, Dusit Niyato, Ping Wang, Ying-Chang Liang, and Dong In Kim. 2019. Applications of deep reinforcement learning in communications and networking: A survey. IEEE Commun. Surv. Tutor. 21, 4 (2019), 3133–3174.

Digital Library

[11]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics. PMLR, 1273–1282.

[12]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, et al. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529–533.

[13]

Sabrina Müller, Onur Atan, Mihaela van der Schaar, and Anja Klein. 2016. Context-aware proactive content caching with service differentiation in wireless networks. IEEE Trans. Wireless Commun. 16, 2 (2016), 1024–1036.

Digital Library

[14]

Tien-Dung Nguyen, Eui-Nam Huh, and Minho Jo. 2018. Decentralized and revised content-centric networking-based service deployment and discovery platform in mobile edge computing for IoT devices. IEEE IoT J. 6, 3 (2018), 4162–4175.

[15]

Zhaolong Ning, Kaiyuan Zhang, Xiaojie Wang, Lei Guo, Xiping Hu, Jun Huang, Bin Hu, and Ricky Y. K. Kwok. 2020. Intelligent edge computing in internet of vehicles: A joint computation offloading and caching solution. IEEE Trans. Intell. Transport. Syst. 22, 4 (2020), 2212–2225.

[16]

Jianli Pan and James McElhannon. 2017. Future edge cloud and edge computing for internet of things applications. IEEE IoT J. 5, 1 (2017), 439–449.

[17]

Gopika Premsankar, Mario Di Francesco, and Tarik Taleb. 2018. Edge computing for the internet of things: A case study. IEEE IoT J. 5, 2 (2018), 1275–1284.

[18]

Qi Qi, Jingyu Wang, Zhanyu Ma, Haifeng Sun, Yufei Cao, Lingxin Zhang, and Jianxin Liao. 2019. Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach. IEEE Trans. Vehic. Technol. 68, 5 (2019), 4192–4203.

[19]

Guanhua Qiao, Supeng Leng, Sabita Maharjan, Yan Zhang, and Nirwan Ansari. 2019. Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks. IEEE IoT J. 7, 1 (2019), 247–257.

[20]

Bowen Shen, Xiaolong Xu, Fei Dar, Lianyong Qi, Xuyun Zhang, and Wanchun Dou. 2020. Dynamic task offloading with minority game for internet of vehicles in cloud-edge computing. In Proceedings of the IEEE International Conference on Web Services (ICWS’20). IEEE, 372–379.

[21]

Osvaldo Simeone. 2018. A very brief introduction to machine learning with applications to communication systems. IEEE Trans. Cogn. Commun. Netw. 4, 4 (2018), 648–664.

[22]

Christos Stergiou, Kostas E. Psannis, Byung-Gyu Kim, and Brij Gupta. 2018. Secure integration of IoT and cloud computing. Fut. Gener. Comput. Syst. 78 (2018), 964–975.

[23]

Zhou Su, Yilong Hui, Qichao Xu, Tingting Yang, Jianyi Liu, and Yunjian Jia. 2018. An edge caching scheme to distribute content in vehicular networks. IEEE Trans. Vehic. Technol. 67, 6 (2018), 5346–5356.

[24]

Csaba Szepesvári. 2010. Algorithms for reinforcement learning. Synth. Lect. Artif. Intell. Mach. Learn. 4, 1 (2010), 1–103.

[25]

Blesson Varghese and Rajkumar Buyya. 2018. Next generation cloud computing: New trends and research directions. Fut. Gener. Comput. Syst. 79 (2018), 849–861.

Digital Library

[26]

Xiaojie Wang, Zhaolong Ning, and Lei Wang. 2018. Offloading in internet of vehicles: A fog-enabled real-time traffic management system. IEEE Trans. Industr. Inf. 14, 10 (2018), 4568–4578.

[27]

Jie Xu, Lixing Chen, and Pan Zhou. 2018. Joint service caching and task offloading for mobile edge computing in dense networks. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’18). IEEE, 207–215.

Digital Library

[28]

Helin Yang, Xianzhong Xie, and Michel Kadoch. 2019. Intelligent resource management based on reinforcement learning for ultra-reliable and low-latency IoV communication networks. IEEE Trans. Vehic. Technol. 68, 5 (2019), 4157–4169.

[29]

Ke Zhang, Supeng Leng, Yejun He, Sabita Maharjan, and Yan Zhang. 2018. Cooperative content caching in 5G networks with mobile edge computing. IEEE Wireless Commun. 25, 3 (2018), 80–87.

[30]

Yu Zhang, Jianguo Yao, and Haibing Guan. 2017. Intelligent cloud resource management with deep reinforcement learning. IEEE Cloud Comput. 4, 6 (2017), 60–69.

[31]

Xiaokang Zhou, Yiyong Hu, Wei Liang, Jianhua Ma, and Qun Jin. 2020. Variational LSTM enhanced anomaly detection for industrial big data. IEEE Trans. Industr. Inf. 17, 5 (2020), 3469–3477.

[32]

Xiaokang Zhou, Yue Li, and Wei Liang. 2020. CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Trans. Comput. Biol. Bioinf. 18, 3 (2020), 912–921.

Digital Library

Cited By

Dai FHossain MWang Y(2025)State of the Art in Parallel and Distributed Systems: Emerging Trends and ChallengesElectronics10.3390/electronics1404067714:4(677)Online publication date: 10-Feb-2025
https://doi.org/10.3390/electronics14040677
Lv JLi KSlowik AJiang H(2025)Distributed Edge Intelligence for Rapid In-Vehicle Medical Emergency Response in Internet of VehiclesIEEE Internet of Things Journal10.1109/JIOT.2024.351694712:5(4750-4760)Online publication date: 1-Mar-2025
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PSDF: Privacy-aware IoV Service Deployment with Federated Learning in Cloud-Edge Computing

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 13, Issue 5

October 2022

424 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3542930

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 13 October 2022

Online AM: 17 August 2022

Accepted: 19 November 2021

Revised: 01 August 2021

Received: 22 March 2021

Published in TIST Volume 13, Issue 5

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Funding Sources

Natural Science Foundation of Jiangsu Province of China
Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps
National Natural Science Foundation of China
Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)

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Cited By

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https://doi.org/10.3390/electronics14040677
Lv JLi KSlowik AJiang H(2025)Distributed Edge Intelligence for Rapid In-Vehicle Medical Emergency Response in Internet of VehiclesIEEE Internet of Things Journal10.1109/JIOT.2024.351694712:5(4750-4760)Online publication date: 1-Mar-2025
https://doi.org/10.1109/JIOT.2024.3516947
Kang HChai ZLi YHuang HZhao Y(2025)Edge computing in Internet of Vehicles: A federated learning method based on Stackelberg dynamic gameInformation Sciences10.1016/j.ins.2024.121452689(121452)Online publication date: Jan-2025
https://doi.org/10.1016/j.ins.2024.121452
Liu BBlancaflor E(2025)Data Security and Privacy Protection Scheme Based on EC-ElGamal in Federal LearningSN Computer Science10.1007/s42979-024-03572-z6:2Online publication date: 14-Feb-2025
https://dl.acm.org/doi/10.1007/s42979-024-03572-z
Andriulo FFiore MMongiello MTraversa EZizzo V(2024)Edge Computing and Cloud Computing for Internet of Things: A ReviewInformatics10.3390/informatics1104007111:4(71)Online publication date: 30-Sep-2024
https://doi.org/10.3390/informatics11040071
Lin H(2024)A secure transaction and smart contract architecture for internet of vehiclesScience Progress10.1177/00368504241296039107:4Online publication date: 26-Nov-2024
https://doi.org/10.1177/00368504241296039
Fu SDong FShen DChen RHao J(2024)DESIGN: Online Device Selection and Edge Association for Federated Synergy Learning-enabled AIoTACM Transactions on Intelligent Systems and Technology10.1145/367323715:5(1-28)Online publication date: 15-Jun-2024
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Yan CZhong WZhai DKhan AGong WXu YXin B(2024)Popularity Bias in Correlation Graph-based API Recommendation for Mashup CreationACM Transactions on Intelligent Systems and Technology10.1145/365444516:1(1-18)Online publication date: 26-Dec-2024
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Gecer MGarbinato B(2024)Federated Learning for Mobility ApplicationsACM Computing Surveys10.1145/363786856:5(1-28)Online publication date: 12-Jan-2024
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Peng KXiao PWang SLeung V(2024)SCOF: Security-Aware Computation Offloading Using Federated Reinforcement Learning in Industrial Internet of Things With Edge ComputingIEEE Transactions on Services Computing10.1109/TSC.2024.337789917:4(1780-1792)Online publication date: Jul-2024
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