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.

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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
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://doi.org/10.1007/s42979-024-03572-z
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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

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
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://doi.org/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
https://dl.acm.org/doi/10.1145/3637868
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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Lu XXiao LXiao YWang WQi NWang Q(2024)Risk-Aware Federated Reinforcement Learning-Based Secure IoV CommunicationsIEEE Transactions on Mobile Computing10.1109/TMC.2024.344701923:12(14656-14671)Online publication date: 1-Dec-2024
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