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Federated Learning (FL) is a rising approach towards collaborative and privacy-preserving machine learning where large-scale medical datasets remain localized to each client.
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Kaissis, G.A., Makowski, M.R., Rückert, D., Braren, R.F.: Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence
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Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: Scaffold: Stochastic controlled averaging for federated learning. In: International conference on machine learning. pp. 5132–5143. PMLR (2020)
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Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems
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Li, X., Gu, Y., Dvornek, N., Staib, L.H., Ventola, P., Duncan, J.S.: Multi-site fmri analysis using privacy-preserving federated learning and domain adaptation: Abide results. Medical Image Analysis
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Charles, Z., Garrett, Z., Huo, Z., Shmulyian, S., Smith, V.: On large-cohort training for federated learning. Advances in neural information processing systems
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Dayan, I., Roth, H.R., Zhong, A., Harouni, A., Gentili, A., Abidin, A.Z., Liu, A., Costa, A.B., Wood, B.J., Tsai, C.S., et al.: Federated learning for predicting clinical outcomes in patients with covid-19. Nature medicine
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Koh, P.W., Sagawa, S., Marklund, H., Xie, S.M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R.L., Gao, I., et al.: Wilds: A benchmark of in-the-wild distribution shifts. In: International Conference on Machine Learning. pp. 5637–5664. PMLR (2021)
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Qu, L., Zhou, Y., Liang, P.P., Xia, Y., Wang, F., Adeli, E., Fei-Fei, L., Rubin, D.: Rethinking architecture design for tackling data heterogeneity in federated learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10061–10071 (2022)
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Zhang, M., Qu, L., Singh, P., Kalpathy-Cramer, J., Rubin, D.L.: Splitavg: A heterogeneity-aware federated deep learning method for medical imaging. IEEE Journal of Biomedical and Health Informatics
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Guo, Y., Guo, K., Cao, X., Wu, T., Chang, Y.: Out-of-distribution generalization of federated learning via implicit invariant relationships (2023)
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Kalra, S., Wen, J., Cresswell, J.C., Volkovs, M., Tizhoosh, H.: Decentralized federated learning through proxy model sharing. Nature communications
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Uddin, M.P., Xiang, Y., Yearwood, J., Gao, L.: Robust federated averaging via outlier pruning. IEEE Signal Processing Letters
2021
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2021
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Yang, C., Wang, Q., Xu, M., Chen, Z., Bian, K., Liu, Y., Liu, X.: Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data. In: Proceedings of the Web Conference 2021. pp. 935–946 (2021)
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Ziller, A., Usynin, D., Braren, R., Makowski, M., Rueckert, D., Kaissis, G.: Medical imaging deep learning with differential privacy. Scientific Reports
2021
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Liu, C., Lou, C., Wang, R., Xi, A.Y., Shen, L., Yan, J.: Deep neural network fusion via graph matching with applications to model ensemble and federated learning. In: International Conference on Machine Learning. pp. 13857–13869. PMLR (2022)
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Ma, X., Zhang, J., Guo, S., Xu, W.: Layer-wised model aggregation for personalized federated learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10092–10101 (2022)
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Nguyen, A.T., Torr, P., Lim, S.N.: Fedsr: A simple and effective domain generalization method for federated learning. Advances in Neural Information Processing Systems
2022
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Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.H., Reina, G.A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al.: Federated learning enables big data for rare cancer boundary detection. Nature communications
2022
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2023
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2023
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Yan, R., Qu, L., Wei, Q., Huang, S.C., Shen, L., Rubin, D., Xing, L., Zhou, Y.: Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging (2023)
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An, X., Shen, L., Hu, H., Luo, Y.: Federated learning with manifold regularization and normalized update reaggregation. Advances in Neural Information Processing Systems
2024
Closest in time.
Puli, A.M., Zhang, L., Wald, Y., Ranganath, R.: Don’t blame dataset shift! shortcut learning due to gradients and cross entropy. Advances in Neural Information Processing Systems
2024
Closest in time.
Zhang, J., Zeng, S., Zhang, M., Wang, R., Wang, F., Zhou, Y., Liang, P.P., Qu, L.: Flhetbench: Benchmarking device and state heterogeneity in federated learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12098–12108 (2024)
2024
Closest in time.