International Journal of Recent
Engineering Science

Research Article | Open Access | Download PDF
Volume 11 | Issue 6 | Year 2024 | Article Id. IJRES-V11I6P114 | DOI : https://doi.org/10.14445/23497157/IJRES-V11I6P114

Federated 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.

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