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Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data.
Learning multiple layers of features from tiny images
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XGBoost: A scalable tree boosting system
T. Chen and C. Guestrin · 2016
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Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
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Distributed deep learning networks among institutions for medical imaging
K. Chang, N. Balachandar, C. Lam, D. Yi, J. Brown, A. Beers, B. Rosen, D. L. Rubin, and J. Kalpathy-Cramer · 2018
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Distributed learning of deep neural network over multiple agents
O. Gupta and R. Raskar · 2018
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Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation
M. J. Sheller, G. A. Reina, B. Edwards, J. Martin, and S. Bakas · 2018
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Parameter-efficient transfer learning for nlp
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly · 2019
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Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al · 2019
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Privacy-preserving federated brain tumour segmentation
W. Li, F. Milletarì, D. Xu, N. Rieke, J. Hancox, W. Zhu, M. Baust, Y. Cheng, S. Ourselin, M. J. Cardoso, et al · 2019
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Federated machine learning: Concept and applications
Q. Yang, Y. Liu, T. Chen, and Y. Tong · 2019
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Flower: A friendly federated learning research framework
D. J. Beutel, T. Topal, A. Mathur, X. Qiu, T. Parcollet, P. P. de Gusmão, and N. D. Lane · 2020
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FedML: A research library and benchmark for federated machine learning
C. He, S. Li, J. So, X. Zeng, M. Zhang, H. Wang, X. Wang, P. Vepakomma, A. Singh, H. Qiu, et al · 2020
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Scaffold: Stochastic controlled averaging for federated learning
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh · 2020
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Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2020
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Ibm federated learning: an enterprise framework white paper v0. 1
H. Ludwig, N. Baracaldo, G. Thomas, Y. Zhou, A. Anwar, S. Rajamoni, Y. Ong, J. Radhakrishnan, A. Verma, M. Sinn, et al · 2020
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Adaptive federated optimization
S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečnỳ, S. Kumar, and H. B. McMahan · 2020
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The future of digital health with federated learning
N. Rieke, J. Hancox, W. Li, F. Milletari, H. R. Roth, S. Albarqouni, S. Bakas, M. N. Galtier, B. A. Landman, K. Maier-Hein, et al · 2020
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Federated learning for breast density classification: A real-world implementation
H. R. Roth, K. Chang, P. Singh, N. Neumark, W. Li, V. Gupta, S. Gupta, L. Qu, A. Ihsani, B. C. Bizzo, et al · 2020
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Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
M. J. Sheller, B. Edwards, G. A. Reina, J. Martin, S. Pati, A. Kotrotsou, M. Milchenko, W. Xu, D. Marcus, R. R. Colen, et al · 2020
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Fed-biomed: A general open-source frontend framework for federated learning in healthcare
S. Silva, A. Altmann, B. Gutman, and M. Lorenzi · 2020
Federated learning improves site performance in multicenter deep learning without data sharing
K. V. Sarma, S. Harmon, T. Sanford, H. R. Roth, Z. Xu, J. Tetreault, D. Xu, M. G. Flores, A. G. Raman, R. Kulkarni, et al · 2021
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Swarm learning for decentralized and confidential clinical machine learning
S. Warnat-Herresthal, H. Schultze, K. L. Shastry, S. Manamohan, S. Mukherjee, V. Garg, R. Sarveswara, K. Händler, P. Pickkers, N. A. Aziz, et al · 2021
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Pysyft: A library for easy federated learning
A. Ziller, A. Trask, A. Lopardo, B. Szymkow, B. Wagner, E. Bluemke, J.-M. Nounahon, J. Passerat-Palmbach, K. Prakash, N. Rose, et al · 2021
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Monai: An open-source framework for deep learning in healthcare
M. J. Cardoso, W. Li, R. Brown, N. Ma, E. Kerfoot, Y. Wang, B. Murrey, A. Myronenko, C. Zhao, D. Yang, et al · 2022
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Flute: A scalable, extensible framework for high-performance federated learning simulations
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Federated learning with matched averaging
H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, and Y. Khazaeni · 2020
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Federated learning for predicting clinical outcomes in patients with covid-19
I. Dayan, H. R. Roth, A. Zhong, A. Harouni, A. Gentili, A. Z. Abidin, A. Liu, A. B. Costa, B. J. Wood, C.-S. Tsai, et al · 2021
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Towards a unified view of parameter-efficient transfer learning
J. He, C. Zhou, X. Ma, T. Berg-Kirkpatrick, and G. Neubig · 2021
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Lora: Low-rank adaptation of large language models
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2021
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The power of scale for parameter-efficient prompt tuning
B. Lester, R. Al-Rfou, and N. Constant · 2021
Cited alongside, same era.
Ditto: Fair and robust federated learning through personalization
T. Li, S. Hu, A. Beirami, and V. Smith · 2021
Cited alongside, same era.
X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang · 2021
Cited alongside, same era.
D. Dimitriadis, M. H. Garcia, D. M. Diaz, A. Manoel, and R. Sim · 2022
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Auto-fedrl: Federated hyperparameter optimization for multi-institutional medical image segmentation
P. Guo, D. Yang, A. Hatamizadeh, A. Xu, Z. Xu, W. Li, C. Zhao, D. Xu, S. Harmon, E. Turkbey, et al · 2022
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Do gradient inversion attacks make federated learning unsafe?
A. Hatamizadeh, H. Yin, P. Molchanov, A. Myronenko, W. Li, P. Dogra, A. Feng, M. G. Flores, J. Kautz, D. Xu, et al · 2022
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MONAI: Medical Open Network for AI, 9 2022
MONAI Consortium · 2022
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Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al · 2022
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Federatedscope: A comprehensive and flexible federated learning platform via message passing
Y. Xie, Z. Wang, D. Chen, D. Gao, L. Yao, W. Kuang, Y. Li, B. Ding, and J. Zhou · 2022
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Closing the generalization gap of cross-silo federated medical image segmentation
A. Xu, W. Li, P. Guo, D. Yang, H. R. Roth, A. Hatamizadeh, C. Zhao, D. Xu, H. Huang, and Z. Xu · 2022
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Reduce communication costs and preserve privacy: Prompt tuning method in federated learning
H. Zhao, W. Du, F. Li, P. Li, and G. Liu · 2022
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Gpt-4 technical report, 2023
OpenAI · 2023
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Private set intersection — Wikipedia, the free encyclopedia, 2023
Wikipedia contributors · 2023
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