approved
Label flipping attacks in Federated Learning

The following experiments showcase Federated Learning using Scikit-learn.

Tags
Data and Resources
To access the resources you must log in
  • FederatedLearning-sklearnipynb

    Jupyter notebook showing the setting of a federated learning loop using...

    The resource: 'FederatedLearning-sklearn' is not accessible as guest user. You must login to access it!
Additional Info
Field Value
Detailed description In this experiment, we showcase a federated training loop using scikit-learn to classify MNIST.Additionally, we show a poisoning attack, namely the label flipping attack, in which attackers change the labels of some objective class to a different class in order to make the global model misclassify one or more classes.Finally, we show some defense mechanisms based on the analysis of user-contributed updates, including a distance-based detection metric, Krum, and median aggregation.
Ethical issues None identified, we used the public MNIST dataset for tests.
Group Ethics and Legality
Involved Institutions Universitat Rovira i Virgili
Involved People Domingo-Ferrer, Josep, josep.domingo@urv.cat, orcid.org/0000-0001-7213-4962
Involved People Blanco-Justicia, Alberto, alberto.blanco@urv.cat, orcid.org/0000-0002-1108-8082
State Complete
Thematic Cluster Visual Analytics [VA]
Thematic Cluster Privacy Enhancing Technology [PET]
system:type Experiment
Management Info
Field Value
Author Blanco Justicia Alberto
Maintainer Blanco Justicia Alberto
Version 1
Last Updated 22 July 2023, 13:52 (CEST)
Created 24 June 2023, 01:09 (CEST)