Post-Processing Fairness Evaluation of Federated Models: An Unsupervised Approach in Healthcare-Reference-Cited by-同舟云学术

Post-Processing Fairness Evaluation of Federated Models: An Unsupervised Approach in Healthcare

Published:2023-07-01 Issue:4 Volume:20 Page:2518-2529
ISSN:1545-5963
Container-title:IEEE/ACM Transactions on Computational Biology and Bioinformatics
language:
Short-container-title:IEEE/ACM Trans. Comput. Biol. and Bioinf.

Author:

Siniosoglou Ilias¹^ORCID,Argyriou Vasileios²^ORCID,Sarigiannidis Panagiotis¹^ORCID,Lagkas Thomas³^ORCID,Sarigiannidis Antonios⁴^ORCID,Goudos Sotirios K.⁵^ORCID,Wan Shaohua⁶^ORCID

Affiliation:

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

2. Department of Networks and Digital Media, Kingston University, Kingston upon Thames, U.K.

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

4. Sidroco Holdings Ltd, Nicosia, Cyprus

5. Physics Department, Aritostle, University of Thessaloniki, Thessaloniki, Greece

6. Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Genetics,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/8857/10214206/10107702.pdf?arnumber=10107702

Reference34 articles.

1. Fairness in federated learning for spatial-temporal applications;mashhadi,2022

2. FairFED: Enabling group fairness in federated learning;ezzeldin,2022

3. A Review of Dimensionality Reduction Techniques for Efficient Computation

4. Algorithmic fairness;pessach,2020

5. Unsupervised Ethical Equity Evaluation of Adversarial Federated Networks

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