Please use this identifier to cite or link to this item:
https://dspace.univ-ouargla.dz/jspui/handle/123456789/33982
Title: | Trust-based in Federated Learning |
Authors: | KHALDI, Amine BOUREGA, Lokmane GHERBI, Leila |
Keywords: | Trust Federated Learning Privacy |
Issue Date: | 2023 |
Publisher: | University Kasdi Merbah Ouargla |
Abstract: | tions and has been widely studied. However, the original FL is still vulnerable to poisoning and inference attacks, which will hinder the landing application of FL. Therefore, it is essential to design a trustworthy federation learning (TFL) to eliminate users’ anxiety. In this paper, we aim to provide a well-researched picture of the security and privacy issues in FL that can bridge the gap to TFL. Firstly, we define the desired goals and critical requirements of TFL, observe the FL model from the perspective of the adversaries and extrapolate the roles and capabilities of potential adversaries backward. Subsequently, we summarize the current mainstream attack and defense means and analyze the charac teristics of the different methods. Based on a priori knowledge, we propose directions for realising the future of TFL that deserve attention |
Description: | People’s Democratic Republic of Algeria Ministry of Higher Education and Scientific Research University Kasdi Merbah Ouargla Faculty of New Technologies of Information and Communication |
URI: | https://dspace.univ-ouargla.dz/jspui/handle/123456789/33982 |
Appears in Collections: | Département d'informatique et technologie de l'information - Master |
Files in This Item:
File | Description | Size | Format | |
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BOUREGA-CHERBI.pdf | 3,9 MB | Adobe PDF | View/Open |
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