Unsupervised Bias Evaluation of DNNs in non-IID Federated Learning Through Latent micro-Manifolds-Reference-Cited by-同舟云学术

Unsupervised Bias Evaluation of DNNs in non-IID Federated Learning Through Latent micro-Manifolds

Published:2022-05-02 Issue: Volume: Page:
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Container-title:IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
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Author:

Siniosoglou Ilias¹,Argyriou Vasileios²,Lagkas Thomas³,Moscholios Ioannis⁴,Fragulis George⁴,Sarigiannidis Panagiotis¹

Affiliation:

1. University of Western Macedonia,Department of Electrical and Computer Engineering,Kozani,Greece

2. Kingston University,Department of Networks and Digital Media,Kingston upon Thames,United Kingdom

3. International Hellenic University,Department of Computer Science,Thessaloniki,Greece

4. University of Peloponnese,Department of Informatics and Telecommunications,Tripoli,Greece

Publisher

IEEE

Link

Reference17 articles.

1. Towards federated learning at scale: System design;bonawitz,2019

3. Unsupervised representation learning with deep convolutional generative adversarial networks;radford,2016

4. Unsupervised discovery of interpretable directions in the gan latent space;voynov;ArXiv,2020

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