Generated Samples from experiment with 7 clients.

Generated Samples from experiment with 7 clients.

<div><p>Deep learning models for diagnostic applications require large amounts of sensitive patient data, raising privacy concerns under centralized training paradigms. We propose FedGAN, a federated learning framework for synthetic medical image generation that combines Generative Adver...

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Bibliographic Details
Main Author: Hassan Kamran (21782056) (author)
Other Authors: Syed Jawad Hussain (18106616) (author), Sohaib Latif (20314613) (author), Imtiaz Ali Soomro (21609078) (author), Mrim M. Alnfiai (19226195) (author), Nouf Nawar Alotaibi (21782059) (author)
Published: 2025
Subjects:
Microbiology
Cell Biology
Genetics
Molecular Biology
Biotechnology
Science Policy
Infectious Diseases
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
Information Systems not elsewhere classified
sensitive patient data
gan &# 8217
centralized training paradigms
abdominal ct scans
silo federated learning
federated learning framework
raising privacy concerns
fedgan generates high
centralized discriminator ).
federated averaging
privacy challenges
realism score
propose fedgan
medical ai
generator via
fedgan achieves
experiments demonstrate
approach pretrains
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