Federated learning generator loss.
<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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2025
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| _version_ | 1852018207943032832 |
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| author | Hassan Kamran (21782056) |
| author2 | Syed Jawad Hussain (18106616) Sohaib Latif (20314613) Imtiaz Ali Soomro (21609078) Mrim M. Alnfiai (19226195) Nouf Nawar Alotaibi (21782059) |
| author2_role | author author author author author |
| author_facet | Hassan Kamran (21782056) Syed Jawad Hussain (18106616) Sohaib Latif (20314613) Imtiaz Ali Soomro (21609078) Mrim M. Alnfiai (19226195) Nouf Nawar Alotaibi (21782059) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hassan Kamran (21782056) Syed Jawad Hussain (18106616) Sohaib Latif (20314613) Imtiaz Ali Soomro (21609078) Mrim M. Alnfiai (19226195) Nouf Nawar Alotaibi (21782059) |
| dc.date.none.fl_str_mv | 2025-07-24T17:48:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0326579.g017 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Federated_learning_generator_loss_/29640277 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | Federated learning generator loss. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <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 Adversarial Networks (GANs) with cross-silo federated learning. Our approach pretrains a DCGAN on abdominal CT scans and fine-tunes it collaboratively across clinical silos using diabetic retinopathy datasets. By federating the GAN’s discriminator and generator via the Federated Averaging (FedAvg) algorithm, FedGAN generates high-quality synthetic retinal images while complying with HIPAA and GDPR. Experiments demonstrate that FedGAN achieves a realism score of 0.43 (measured by a centralized discriminator). This work bridges data scarcity and privacy challenges in medical AI, enabling secure collaboration across institutions.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_e2ef9805e7c36c20edf4b13b4d48dbce |
| identifier_str_mv | 10.1371/journal.pone.0326579.g017 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29640277 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Federated learning generator loss.Hassan Kamran (21782056)Syed Jawad Hussain (18106616)Sohaib Latif (20314613)Imtiaz Ali Soomro (21609078)Mrim M. Alnfiai (19226195)Nouf Nawar Alotaibi (21782059)MicrobiologyCell BiologyGeneticsMolecular BiologyBiotechnologyScience PolicyInfectious DiseasesBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsensitive patient datagan &# 8217centralized training paradigmsabdominal ct scanssilo federated learningfederated learning frameworkraising privacy concernsfedgan generates highcentralized discriminator ).federated averagingprivacy challengesrealism scorepropose fedganmedical aigenerator viafedgan achievesexperiments demonstrateapproach pretrains<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 Adversarial Networks (GANs) with cross-silo federated learning. Our approach pretrains a DCGAN on abdominal CT scans and fine-tunes it collaboratively across clinical silos using diabetic retinopathy datasets. By federating the GAN’s discriminator and generator via the Federated Averaging (FedAvg) algorithm, FedGAN generates high-quality synthetic retinal images while complying with HIPAA and GDPR. Experiments demonstrate that FedGAN achieves a realism score of 0.43 (measured by a centralized discriminator). This work bridges data scarcity and privacy challenges in medical AI, enabling secure collaboration across institutions.</p></div>2025-07-24T17:48:00ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0326579.g017https://figshare.com/articles/figure/Federated_learning_generator_loss_/29640277CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296402772025-07-24T17:48:00Z |
| spellingShingle | Federated learning generator loss. Hassan Kamran (21782056) 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 |
| status_str | publishedVersion |
| title | Federated learning generator loss. |
| title_full | Federated learning generator loss. |
| title_fullStr | Federated learning generator loss. |
| title_full_unstemmed | Federated learning generator loss. |
| title_short | Federated learning generator loss. |
| title_sort | Federated learning generator loss. |
| topic | 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 |