Open Access
Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
|
|
---|---|---|
Article Number | 03030 | |
Number of page(s) | 9 | |
Section | Image Processing and Computer Vision | |
DOI | https://doi.org/10.1051/itmconf/20257003030 | |
Published online | 23 January 2025 |
- J. Qu, K. Lin, Y. Wu, I. Y. Sun, Fear and perceived risk of cyber fraud victimization among Chinese University students. Crime, Law and Social Change, 1–20 (2024). [Google Scholar]
- S. Shiyang, Study on Telecommunication Fraud from a Student’s Perspective. International Journal of Frontiers in Sociology, 5(16) (2023). [Google Scholar]
- W. Yang, Y. Zhang, K. Ye, et al., FFd: A federated learning based method for credit card fraud detection. In Proceedings of the Big Data-BigData 2019, Held as Part of the Services Conference Federation, San Diego, CA, USA, June 25-30, 18-32 (2019). [CrossRef] [Google Scholar]
- M. A. Salam, K. M. Fouad, D. L. Elbably, S. M. Elsayed, Federated learning model for credit card fraud detection with data balancing techniques. Neural Computing and Applications, 36, 6231-6256 (2024). [CrossRef] [Google Scholar]
- V. V. Krishna Reddy, R. V. Kumar Reddy, et al., Deep learning-based credit card fraud detection in federated learning. Expert Systems With Applications, 255, 124493 (2024). [CrossRef] [Google Scholar]
- L. Li, Y. Fan, M. Tse, et al., A review of applications in federated learning. Computers & Industrial Engineering, 149, 106854 (2020). [CrossRef] [Google Scholar]
- C. Zhang, Y. Xie, H. Bai, et al., A survey on federated learning. Knowledge-Based Systems, 216, 106775 (2021). [CrossRef] [Google Scholar]
- M. Ye, X. Fang, B. Du, et al., Heterogeneous federated learning: State-of-the-art and research challenges. ACM Computing Surveys, 56(3), 1–44 (2023). [Google Scholar]
- M. Yurochkin, M. Agarwal, S. Ghosh, et al., Bayesian nonparametric federated learning of neural networks. In International Conference on Machine Learning. PMLR, 72527261 (2019). [Google Scholar]
- Y. Zhao, M. Li, L. Lai, et al., Federated learning with non-iid data. arXiv preprint arXiv, 1806.00582 (2022). [Google Scholar]
- R. Haffar, D. Sanchez, J. Domingo-Ferrer, Explaining predictions and attacks in federated learning via random forests. Applied Intelligence, 53(1), 169–185 (2023). [CrossRef] [Google Scholar]
- Y. Liu, Y. Liu, Z. Liu, et al., Federated forest. IEEE Transactions on Big Data, 8 (3), 843854 (2020). [Google Scholar]
- A. Poudyal, U. Tamrakar, R. D. Trevizan, et al., Multiarea inertia estimation using convolutional neural networks and federated learning. IEEE Systems Journal, 16(4), 6401–6412 (2021). [Google Scholar]
- H. Zhu, Y. Jin, Multi-objective evolutionary federated learning. IEEE Transactions on Neural Networks and Learning Systems, 31(4), 1310–1322 (2019). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.