A Systematic Analysis of Federated Learning

Chaudhari, A. and Hitham Seddig, A.A. and Sarlan, A. and Raut, R. (2022) A Systematic Analysis of Federated Learning. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Deep Learning is an emerging area which is applicable to deal with massive and high multi-dimensional data in various E-Commerce platforms. This data is called as Big Data which is available in the complex and unstructured format, in terms of Volume, Velocity, Validity, Veracity, Variety and Variability. The data used in such e-platforms, is revealed at multiple places affecting the User's private data and security. To overcome the issue of user's private data security, a new dimension of Deep Learning Approach is been introduced, which is called as 'Federated Learning (FL)'. Federated Learning provides Privacy Preservation and security to the User's data, thereby enhancing the accuracy of the system. This paper discusses about FL, various types of FL along with the implementation results of FL with Matrix Factorization (For accurate recommendations) on Movielens 1M dataset 15. The paper concludes with various FL threats, and applications. © 2022 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 0; Conference of 2022 International Conference on Digital Transformation and Intelligence, ICDI 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:185994
Uncontrolled Keywords: Big data; Clustering algorithms; Collaborative filtering; Deep learning; Matrix algebra; Matrix factorization, Commerce platforms; E-platforms; Federated learning; Matrix factorizations; Means square errors; Multidimensional data; Private data; Systematic analysis; Tensor flow federated; Volume velocities, Mean square error
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 19 Dec 2023 03:23
Last Modified: 19 Dec 2023 03:23
URI: https://khub.utp.edu.my/scholars/id/eprint/17302

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