journal article·2025Open access

Generated Preserved Adversarial Federated Learning for Enhanced Image Analysis (GPAF)

Sanaa Lakrouni, Slimane Bah, Marouane Sebgui

Computers, Materials & Continua· Volume 85· Issue 3

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Privacy-Preserving Technologies in Data · Brain Tumor Detection and Classification · Face recognition and analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (37)

Show source-provided references
1. The future of digital health with federated learning10.1038/s41746-020-00323-1 ↗
2. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging10.1109/TMI.2022.3233574 ↗
3. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data10.1038/s41598-020-69250-1 ↗
4. Federated learning for predicting clinical outcomes in patients with COVID-1910.1038/s41591-021-01506-3 ↗
5. A federated learning framework for breast cancer histopathological image classification10.3390/electronics11223767 ↗
6. McMahan HB, Moore E, Ramage D, Hampson S, Arcas BA. Communication-efficient learning of deep networks from decentralized data. arXiv:1602.05629. 2016. doi:10.48550/ARXIV.1602.05629.
7. Guan H, Liu M. Domain adaptation for medical image analysis: a survey. arXiv:2102.09508. 2021. doi:10.48550/ARXIV.2102.09508.
8. Domain generalization: a survey
9. Liu Q, Chen C, Qin J, Dou Q, Heng PA. FedDG: federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. arXiv:2103.06030. 2021. doi:10.48550/ARXIV.2103.06030.10.1109/CVPR46437.2021.00107 ↗
10. Peng X, Huang Z, Zhu Y, Saenko K. Federated adversarial domain adaptation. arXiv:1911.02054. 2019. doi:10.48550/ARXIV.1911.02054.
11. Hitaj B, Ateniese G, Perez-Cruz F. Deep models under the GAN: information leakage from collaborative deep learning. arXiv:1702.07464. 2017. doi:10.48550/ARXIV.1702.0746.10.1145/3133956.3134012 ↗
12. Model inversion attacks that exploit confidence information and basic countermeasures
13. Garg S, Erickson N, Sharpnack J, Smola A, Balakrishnan S, Lipton ZC. RLSbench: domain adaptation under relaxed label shift. arXiv:2302.03020. 2023. doi:10.48550/ARXIV.2302.03020.
14. Zhu Z, Hong J, Zhou J. Data-free knowledge distillation for heterogeneous federated learning. arXiv:2105.10056. 2021. doi:10.48550/ARXIV.2105.10056.
15. Karimireddy SP, Kale S, Mohri M, Reddi SJ, Stich SU, Suresh AT. SCAFFOLD: stochastic controlled averaging for federated learning. arXiv:1910.06378. 2019. doi:10.48550/ARXIV.1910.06378.
16. Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V. Federated optimization in heterogeneous networks. arXiv:1812.06127. 2018. doi:10.48550/ARXIV.1812.06127.
17. Collaborative optimization and aggregation for decentralized domain generalization and adaptation
18. Chen J, Jiang M, Dou Q, Chen Q. Federated domain generalization for image recognition via cross-client style transfer. arXiv:2210.00912. 2022. doi:10.48550/ARXIV.2210.00912.10.1109/WACV56688.2023.00044 ↗
19. Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, et al. Domain-adversarial training of neural networks. arXiv:1505.07818. 2015. doi:10.48550/ARXIV.1505.07818.
20. Sun Y, Chong N, Ochiai H. Feature distribution matching for federated domain generalization. arXiv:2203.11635. 2022. doi:10.48550/ARXIV.2203.11635.10.36227/techrxiv.19575760 ↗
21. Federated transfer learning for intelligent fault diagnostics using deep adversarial networks with data privacy10.1109/TMECH.2021.3065522 ↗
22. Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions10.1177/14759217211029201 ↗
23. Lin T, Kong L, Stich SU, Jaggi M. Ensemble distillation for robust model fusion in federated learning. arXiv:2006.07242. 2020. doi:10.48550/ARXIV.2006.07242.
24. HarmoFL: harmonizing local and global drifts in federated learning on heterogeneous medical images
25. Generalizing to unseen domains via adversarial data augmentation
26. Supervised representation learning: transfer learning with deep autoencoders
27. Kingma DP, Welling M. Auto-encoding variational bayes. arXiv:1312.6114. 2013. doi:10.48550/ARXIV.1312.6114.
28. Mao Q, Lee HY, Tseng HY, Ma S, Yang MH. Mode seeking generative adversarial networks for diverse image synthesis. arXiv:1903.05628. 2019. doi:10.48550/ARXIV.1903.05628.10.1109/CVPR.2019.00152 ↗
29. Srivastava A, Valkov L, Russell C, Gutmann MU, Sutton C. VEEGAN: reducing mode collapse in GANs using implicit variational learning. arXiv:1705.07761. 2017. doi:10.48550/ARXIV.1705.07761.
30. Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B. Adversarial autoencoders. arXiv:1511.05644. 2015. doi:10.48550/ARXIV.1511.05644.
31. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. arXiv:1406.2661. 2014. doi:10.48550/ARXIV.1406.2661.
32. FedSR: a simple and effective domain generalization method for federated learning
33. Dataset of breast ultrasound images10.1016/j.dib.2019.104863 ↗
34. Di Salvo F, Doerrich S, Ledig C. MedMNIST-C: comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions. arXiv:2406.17536. 2024. doi:10.48550/ARXIV.2406.17536.
35. Beutel DJ, Topal T, Mathur A, Qiu X, Fernandez-Marques J, Gao Y, et al. Flower: a friendly federated learning research framework. arXiv:2007.14390. 2020. doi:10.48550/ARXIV.2007.14390.
36. Robust aggregation for federated learning10.1109/TSP.2022.3153135 ↗
37. Impact of aggregation function randomization against model poisoning in federated learning