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In recent years, One-shot Federated Learning methods based on Diffusion Models have garnered increasing attention due to their remarkable performance.
Practical one-shot federated learning for cross-silo setting
Li, Q., He, B., and Song, D · 2010
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Earlier work this paper cites.
Guha, N., Talwalkar, A., and Smith, V · 2019
Earlier work this paper cites.
Moment matching for multi-source domain adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B · 2019
Earlier work this paper cites.
The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al · 2020
Earlier work this paper cites.
Ensemble distillation for robust model fusion in federated learning
Lin, T., Kong, L., Stich, S. U., and Jaggi, M · 2020
Earlier work this paper cites.
Distilled one-shot federated learning
Zhou, Y., Pu, G., Ma, X., Li, X., and Wu, D · 2020
Earlier work this paper cites.
Federated learning based on dynamic regularization
Acar, D. A. E., Zhao, Y., Navarro, R. M., Mattina, M., Whatmough, P. N., and Saligrama, V · 2021
Earlier work this paper cites.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al · 2021
Cited alongside, same era.
Practical one-shot federated learning for cross-silo setting
Li, Q., He, B., and Song, D · 2021
Cited alongside, same era.
Federated learning: Opportunities and challenges
Mammen, P. M · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
Feddrive: Generalizing federated learning to semantic segmentation in autonomous driving
Cross-domain federated adaptive prompt tuning for clip
Su, S., Yang, M., Li, B., and Xue, X · 2022
Later among the works it cites.
Phoenix: A federated generative diffusion model
Jothiraj, F. V. S. and Mashhadi, A · 2023
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Li, J., Li, D., Savarese, S., and Hoi, S · 2023
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One-shot federated learning without server-side training
Su, S., Li, B., and Xue, X · 2023
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Exploring one-shot semi-supervised federated learning with a pre-trained diffusion model
Yang, M., Su, S., Li, B., and Xue, X · 2023
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Fantauzzo, L., Fanì, E., Caldarola, D., Tavera, A., Cermelli, F., Ciccone, M., and Caputo, B · 2022
Cited alongside, same era.
Data-free one-shot federated learning under very high statistical heterogeneity
Heinbaugh, C. E., Luz-Ricca, E., and Shao, H · 2022
Cited alongside, same era.
Deep federated learning for autonomous driving
Nguyen, A., Do, T., Tran, M., Nguyen, B. X., Duong, C., Phan, T., Tjiputra, E., and Tran, Q. D · 2022
Cited alongside, same era.
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V
Cited in the paper.
Dense: Data-free one-shot federated learning
Zhang, J., Chen, C., Li, B., Lyu, L., Wu, S., Ding, S., Shen, C., and Wu, C
Cited in the paper.
Nico++: Towards better benchmarking for domain generalization
Zhang, X., Zhou, L., Xu, R., Cui, P., Shen, Z., and Liu, H
Cited in the paper.
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Federated generative learning with foundation models
Zhang, J., Qi, X., and Zhao, B · 2023
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Feddeo: Description-enhanced one-shot federated learning with diffusion models
Yang, M., Su, S., Li, B., and Xue, X · 2024
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