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Tackling Non-IID Data and Data Poisoning in Federated Learning Using Adversarial Synthetic Data Cover

Tackling Non-IID Data and Data Poisoning in Federated Learning Using Adversarial Synthetic Data

Journal of Automation, Mobile Robotics and Intelligent Systems
Volume 18 (2024): Issue 3 (September 2024)
By: Anastasiya Danilenka  
Open Access
|Sep 2024

Authors

Anastasiya Danilenka

anastasiya.danilenka.dokt@pw.edu.pl

Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
DOI: https://doi.org/10.14313/jamris/3-2024/17 | Journal eISSN: 2080-2145 | Journal ISSN: 1897-8649
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Language: English
Page range: 1 - 13
Submitted on: Dec 27, 2023
Accepted on: Mar 11, 2024
Published on: Sep 12, 2024
Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
federated learning,
non-IID data,
label skew,
data poisoning,
label flipping
Related subjects:
Computer sciences,
Artificial intelligence,
Engineering,
Electrical engineering,
Control engineering, metrology and testing,
Mechanical engineering,
Fundamentals of mechanical engineering

© 2024 Anastasiya Danilenka, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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