Research Article | Open Access | Download PDF
Volume 11 | Issue 6 | Year 2024 | Article Id. IJRES-V11I6P114 | DOI : https://doi.org/10.14445/23497157/IJRES-V11I6P114Federated Learning: Privacy-Preserving Data Science
M. Micheal Mithra, C. Nattar Devi, V. Preethika, K. Vasukidevi, P. Vidhya Lakshmi
Received | Revised | Accepted | Published |
---|---|---|---|
18 Oct 2024 | 22 Nov 2024 | 11 Dec 2024 | 28 Dec 2024 |
Citation :
M. Micheal Mithra, C. Nattar Devi, V. Preethika, K. Vasukidevi, P. Vidhya Lakshmi, "Federated Learning: Privacy-Preserving Data Science," International Journal of Recent Engineering Science (IJRES), vol. 11, no. 6, pp. 173-177, 2024. Crossref, https://doi.org/10.14445/23497157/IJRES-V11I6P114
Abstract
A new paradigm in machine learning called federated learning allows for decentralized data processing. At the same time, it protects users’ privacy. Centralized data learning reduces the risk of data breaches and illegal access. The model can study multiple devices or data assets without sending raw logs. The basic idea of federated consciousness, its structure, and the special algorithms used to guarantee a powerful version of its education are tested in this e-book and packages that travel in sectors such as healthcare, banking, and equipment. Already a genius, We also examine challenges related to the differences noted. Model convergence and efficiency of verbal exchanges: this study aims to demonstrate the importance of federated knowledge acquisition in developing privacy-preserving information science. Moreover, it creates the gateway for a safer and more collaborative IA structure. It conducts rigorous evaluations of new research and research cases.
Keywords
Privacy, Data science, Accuracy, Model, Data mining.
References
[1] B.S. Mahadevaswamy, “Further Results on Strongly Perfect Graphs,” International Journal of Research in Engineering and Science vol. 11, no. 1, pp. 13-19, 2023.
[Publisher Link]
[2] Kamran Ahmad Awan et al., “Privacy-Preserving Big Data Security for IoT With Federated Learning and Cryptography,” IEEE Access, vol. 11, pp. 120918-120934, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Yang Han, “A Privacy Preserving Federated Learning System for IoT Devices Using Blockchain and Optimization,” Journal of Computer and Communications, vol. 12, no. 9, pp. 78-102, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Nsie Erimola María Reina Agripina, and Blessed Shinga Mafukidze, “Advances, Challenges & Recent Developments in Federated Learning,” Open Access Library Journal, vol. 11, no. 10, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Konan Martin, Wenyong Wang, and Brighter Agyemang, “Optimized Homomorphic Scheme on Map Reduce for Data Privacy Preserving,” Journal of Information Security, vol. 8, no. 3, pp. 1-17, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Nguyen Truong et al., “Privacy Preservation in Federated Learning: An Insightful Survey from the GDPR Perspective,” Computers & Security, vol. 110, pp. 1-23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Hongbin Fan, Changbing Huang, and Yining Liu, “Federated Learning-Based Privacy-Preserving Data Aggregation Scheme for IIoT,” IEEE Access, vol. 11, pp. 6700-6707, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Malgorzata Smietanka, Hirsh Pithadia, and Philip Treleaven, “Federated Learning for Privacy-Preserving Data Access,” International Journal of Data Science and Big Data Analytics, vol. 1, no. 2, pp. 1-13, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Shiwei Sun et al., “Understanding the Factors Affecting the Organizational Adoption of Big Data,” Journal of Computer Information Systems, vol. 58, no. 3, pp. 193-203, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Kallakunta Ravi Kumar, “Federated Learning: Pioneering Privacy-Preserving Data Analysis,” IJFANS International Journal of Food and Nutritional Sciences, vol. 8, no. 1, pp. 654-660, 2019.
[Publisher Link]