Please use this identifier to cite or link to this item:
https://dspace.univ-ouargla.dz/jspui/handle/123456789/35008
Title: | Trust-based in Federated Learning |
Authors: | Boukhamla, Akram Zine Eddine BOUREGA, Lokmane GHERBI, Leila |
Keywords: | Trust Federated Learning Privacy |
Issue Date: | 2023 |
Publisher: | UNIVERSITY OF KASDI MERBAH OUARGLA |
Abstract: | Federated learning (FL) provides convenience for cross-domain machine learning applicationsandhasbeenwidelystudied. However,theoriginalFLisstillvulnerabletopoisoning 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 characteristics of the different methods. Based on a priori knowledge, we propose directions for realizing the future of TFL that deserve attention. |
URI: | https://dspace.univ-ouargla.dz/jspui/handle/123456789/35008 |
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-GHERBI.pdf | 3,9 MB | Adobe PDF | View/Open |
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