Federated Learning: Privacy-Preserving Data Science
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International Journal of Recent Engineering Science (IJRES) |  |
| © 2024 by IJRES Journal | ||
| Volume-11 Issue-6 |
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| Year of Publication : 2024 | ||
| Authors : M. Micheal Mithra, C. Nattar Devi, V. Preethika, K. Vasukidevi, P. Vidhya Lakshmi |
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| DOI : 10.14445/23497157/IJRES-V11I6P114 |
How to Cite?
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, 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.
Reference
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