The DOI matches its Crossref record, with a small difference in title or year; 5 of the 5 references it cites were found in a registry or an index.
FedVPS: Federated Learning for Privacy and Security of Internet of Vehicles on Non-IID Data
Abstract
The 6th generation of mobile networks (6G) offers many advantages, including fast transmission rates, low delays, and ultra-dense networks. These benefits can solve the communication problems in the Internet of Vehicles (IoV). However, auto-mobiles as IoV terminals generate Non-Independent Identically Distributed (Non-IID) data while driving, making it necessary to introduce distributed machine learning into the IoV as a network that integrates intelligent computing and vehicle networking. To address the Non-IID of local data and the heterogeneity of local models, we propose a heterogeneous Federated Learning scheme in this paper. Based on the distributed architecture (Terminal - Edge device - Cloud) in the IoV, we designed a privacy protection scheme using Secure Multi-Party Computation (SMPC). This ensures that the terminals participating in Federated Learning get accurate calculation results without revealing useful information, thus preserving the privacy of local datasets. The privacy and security of the IoV based on Federated Learning (FedVPS) not only protect the privacy of the terminals but also improve communication efficiency, enabling accurate and efficient distributed machine learning. The aggregation method of Federated Learning is a prototype-based scheme that utilizes the effective information stored in local datasets. In the BIT-Vehicle dataset, FedVPS is not only more robust but also has excellent prediction accuracy. Compared to FedAvg, FedVPS has advantages…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“It also addresses privacy threats, including reconstruction and membership inference attacks.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [47]
In the citing paper
“By integrating SMPC with differential privacy, FedVPS addressed non-IID data and model heterogeneity and used prototype-based aggregation to reduce communication costs.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [47]
In the citing paper
“Another study [47] proposed FedVPS, a framework leveraging SMPC to enhance privacy and security in FL for IoV.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [47]
Citations
Cites
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Erlingsson, Ú., Pihur, V., & Korolova, A. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660348 · Cited by 1,838
Data Poisoning Attacks Against Federated Learning Systems
Tolpegin, V., Truex, S., Gursoy, M. E., & Liu, L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58951-6_24 · Cited by 927
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches
Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2954595 · Cited by 620
Federated Learning for Keyword Spotting
Leroy, D., Coucke, A., Lavril, T., Gisselbrecht, T., & Dureau, J. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683546 · Cited by 291
Research works printed but not linked: 28
- Apple, 2017, Learning with privacy at scale, Apple Machine Learning Journal, 1.8
- Sundar, 2019, Privacy should not be a luxury good, The New York Times, 7
- Andrew, 2018, federated learning for mobile keyboard prediction, arXiv preprint
- Timothy, 2018, Applied federated learning: Improving google keyboard query suggestions, arXiv preprint
- Mingqing, 2019, Federated learning of out-of-vocabulary words, arXiv preprint
- Swaroop, 2019, Federated learning for emoji prediction in a mobile keyboard, arXiv preprint
- Knaan, 2017, Under the hood of the Pixel 2: how AI is super-charging hardware, Google AI
- Brouwer, 2019, The federated future is ready for shipping
- Xavier, 2016, A roadmap to safer driving through advanced driver assistance systems, Auto Tech Review, 5.7, 20
- Hairi, 2016, Current collision mitigation technologies for advanced driver assistance systems-a survey, Perintise Journal, 6.2, 78
- Kenneth, 2009, VANET: vehicular applications and inter-networking technologies
- 10.1109/TVT.2020.2973651 · DOI · Paper page
- Ligeng, 2019, Deep leakage from gradients, Advances in neural information processing systems, 32
- 10.1109/SP.2019.00065 · DOI · Paper page
- Ivan, 2001, A generalisation, a simplification and some applications of Pailliers probabilistic public-key system
- Cynthia, 2014, The algorithmic foundations of differential privacy, Foundations and Trends® in The-oretical Computer Science, 9.3–4, 211
- 10.1109/COMPSAC51774.2021.00070 · DOI
- Sonal, 2020, Turn signal prediction: A federated learning case study, arXiv preprint
- 10.1016/j.vehcom.2021.100396 · DOI
- 10.1155/2021/6633332 · DOI
- 10.1109/ICRA48506.2021.9561612 · DOI · Paper page
- 10.1109/GLOCOM.2018.8647927 · DOI · Paper page
- 10.1145/3133956.3133982 · DOI · Paper page
- 10.1145/2810103.2813687 · DOI · Paper page
- Nguyen, 2016, Collecting and analyzing data from smart device users with local differential privacy, arXiv preprint
- Lingjuan, 2020, Privacy and robustness in fed-erated learning: Attacks and defenses, arXiv preprint
- Lichao, 2020, Federated model distillation with noise-free differential privacy, arXiv preprint
- Yue, 2022, Fedproto: Federated prototype learning across heterogeneous clients, 1
Cited by
Sorted by citations
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 9
“This ensures that no single party has access to the complete dataset, maintaining privacy across the board.”
Metadata
- Authors
- Hangdong, K., Bo, M., Darong, H., & Zhaoyang, D.
- Affiliations
- Chongqing Jiaotong University (CN)
- College of Information Science and Engineering, Chongqing Jiaotong University,Chongqing,P. R. China,400074
- Anhui University (CN)
- Venue
- 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)
- Publisher
- IEEE
- Published
- 2023-05-12
- Pages
- 178-183
- OpenAlex
- W4383502492
- Cited by
- 6
- References
- 5
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3
- FWCI
- 1.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Data Driven Control and Learning Systems Conference (DDCLS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- federated learning, Internet of Vehicles, non-IID data, privacy preservation, secure multi-party computation, heterogeneous federated learning, model aggregation, 6G, communication efficiency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
