FedRD: Privacy-preserving adaptive Federated learning framework for intelligent hazardous Road Damage detection and warning
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Study [172] proposed an adaptive FL framework for detecting hazardous road damage and classifying it by severity.
“Similarly, [172] proposed an adaptive FL framework for detecting hazardous road damage, classifying it by severity.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [172]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Part of this paper’s reference list could not be split into single entries.
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Cha, Y., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12334 · Cited by 1,558 · see citing works
An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features
He, Y., Song, K., Meng, Q., & Yan, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2915404 · Cited by 1,341 · cited 1× · see citing works
“The authors of [18] introduced a multilevel-feature fusion network (MFN), which fused multilevel hierarchical features from di erent layers of the CNN backbone into the same dimension for defect recognition.”
BackgroundKoch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015) · Advanced Engineering Informatics · doi:10.1016/j.aei.2015.01.008 · Cited by 1,065 · see citing works
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., & Pedarsani, R. (2019) · arXiv · doi:10.48550/arxiv.1909.13014 · Cited by 587 · see citing works
Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review
Gopalakrishnan, K. (2018) · Data · doi:10.3390/data3030028 · Cited by 210 · see citing works
Flagged first, then by citations
Research works printed but not linked: 27
- [1][1] Anaissi, A., Khoa, N.L.D., Rakotoarivelo, T., Alamdari, M.M., Wang, Y., 2019. Smart pothole detection system using vehicle-mounted sensors and machine learning. Journal of Civil Structural Health Monitoring 9, 91-102. -
- [2][2] Bai, X., Fang, Y., Lin, W., Wang, L., Ju, B.F., 2014. Saliency-based defect detection in industrial images by using phase spectrum. IEEE Transactions on Industrial Informatics 10, 2135-2145. -
- [3][3] Basudan, S., Lin, X., Sankaranarayanan, K., 2017. A privacypreserving vehicular crowdsensing-based road surface condition monitoring system using fog computing. IEEE Internet of Things Journal 4, 772-782. -
- [4][4] Cao, J., Yang, G., Yang, X., 2020. A pixel-level segmentation convolutional neural network based on deep feature fusion for surface defect detection. IEEE Transactions on Instrumentation and Measurement 70, 1-12. -
- [6][6] Chellaswamy, C., Famitha, H., Anusuya, T., Amirthavarshini, S., 2018. Iot based humps and pothole detection on roads and information sharing, in: 2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), IEEE. pp. 084-090. -
- [9][9] Fan, L., 2018. Image pixelization with di erential privacy, in: IFIP Annual Conference on Data and Applications Security and Privacy, Springer. pp. 148-162. -
- [10][10] Fan, R., Bocus, M.J., Zhu, Y., Jiao, J., Wang, L., Ma, F., Cheng, S., Liu, M., 2019. Road crack detection using deep convolutional neural network and adaptive thresholding, in: 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE. pp. 474-479. -
- [11][11] Fauzi, A., Utaminingrum, F., Ramdani, F., 2020. Road surface classification based on lbp and glcm features using knn classifier. Bulletin of Electrical Engineering and Informatics 9, 1446-1453. -
- [12][12] Fox, A., Kumar, B.V., Chen, J., Bai, F., 2017. Multi-lane pothole detection from crowdsourced undersampled vehicle sensor data. IEEE Transactions on Mobile Computing 16, 3417-3430. -
- [13][13] Geiping, J., Bauermeister, H., Dröge, H., Moeller, M., 2020. Inverting gradients-how easy is it to break privacy in federated learning? arXiv preprint arXiv:2003.14053 . - · arXiv
- [16][16] Guha, N., Talwalkar, A., Smith, V., 2019. One-shot federated learning. arXiv preprint arXiv:1902.11175 . - · arXiv
- [19][19] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., 2017. Mobilenets: E cient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 . - · arXiv
- [20][20] Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S., 2013. Salient object detection: A discriminative regional feature integration approach, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2083-2090. -
- [21][21] Kairouz, P., Oh, S., Viswanath, P., 2014. Extremal mechanisms for local di erential privacy, in: Advances in neural information processing systems, pp. 2879-2887. -
- [22][22] Kawano, M., Yonezawa, T., Nakazawa, J., 2017. Deep on edge: Opportunistic road damage detection with city o cial vehicles, in: Proceedings of The Third International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems (SPWID 2017). -
- [25][25] Kone n` y, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D., 2016. Federated learning: Strategies for improving communication e ciency. arXiv preprint arXiv:1610.05492 . - · arXiv
- [28][28] Masci, J., Meier, U., Fricout, G., Schmidhuber, J., 2013. Multiscale pyramidal pooling network for generic steel defect classification, in: The 2013 International Joint Conference on Neural Networks (IJCNN), IEEE. pp. 1-8. -
- [30][30] Seif, M., Tandon, R., Li, M., 2020. Wireless federated learning with local di erential privacy, in: 2020 IEEE International Symposium on Information Theory (ISIT), IEEE. pp. 2604-2609. -
- [31][31] Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 . - · arXiv
- [33][33] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9. -
- [35][35] Wang, J., Luo, L., Ye, W., Zhu, S., 2020a. A defect-detection method - of split pins in the catenary fastening devices of high-speed railway based on deep learning. IEEE Transactions on Instrumentation and Measurement 69, 9517-9525. -
- [36][36] Wang, J., Wang, Z., Gao, C., Sang, N., Huang, R., 2016. Deeplist: Learning deep features with adaptive listwise constraint for person reidentification. IEEE Transactions on Circuits and Systems for Video Technology 27, 513-524. -
- [37][37] WANG, R.b., LI, L.h., JIN, L.s., GUO, L., ZHAO, Y.b., 2007. Study on binocular vision based obstacle detection technology for intelligent vehicle. Journal of Image and Graphics 12. -
- [38][38] Wang, T., Mei, Y., Jia, W., Zheng, X., Wang, G., Xie, M., 2020b. Edge-based di erential privacy computing for sensor-cloud systems. Journal of Parallel and Distributed computing 136, 75-85. -
- [39][39] Wang, Y., Ding, Y., Wu, Q., Wei, Y., Qin, B., Wang, H., 2018. Privacy-preserving cloud-based road condition monitoring with source authentication in vanets. IEEE Transactions on Information Forensics and Security 14, 1779-1790. -
- [40][40] Xie, X., Mirmehdi, M., 2007. Texems: Texture exemplars for defect detection on random textured surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1454-1464. -
- [41][41] Yuan, Y., Islam, M.S., Yuan, Y., Wang, S., Baker, T., Kolbe, L.M., 2020. Ecrd: Edge-cloud computing framework for smart road damage detection and warning. IEEE Internet of Things Journal , 1-1. -
Cited by
Sorted by citations
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 1 · see citing works
“Similarly, [172] proposed an adaptive FL framework for detecting hazardous road damage, classifying it by severity.”
- Authors
- Yuan, Y., Yuan, Y., Baker, T., Kolbe, L. M., & Hogrefe, D.
- Affiliations
- University of Göttingen (DE)
- University of Sharjah (AE)
- Corresponding author
- Yuan, Yali
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 125
- Pages
- 385-398
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W3174168901
- Cited by
- 42
- References
- 7
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 14 · 2023: 5 · 2022: 3 · 2021: 1
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- green
- cris.brighton.ac.uk
- Licence
- Locations
- Future Generation Computer Systems (publishedVersion)
- University of Brighton Repository (University of Brighton) (submittedVersion, cc-by-nc-nd)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion)
- Funders
- China Scholarship Council (201706050095)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- road damage detection, privacy-preserving federated learning, edge computing, cloud-edge collaboration, differential privacy, pixelation, adaptive federated learning, hierarchical feature fusion, road safety
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
