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Federated learning allows multiple clients to collaborate to train high-performance deep learning models while keeping the training data locally.
Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H.; Qi, H.; and Brown, M. 2019 · 1909
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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. 2019 · 1912
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Visualizing Data using t-SNE
van der Maaten, L.; and Hinton, G. 2008 · 2008
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Federated unsupervised representation learning
Zhang, F.; Kuang, K.; You, Z.; Shen, T.; Xiao, J.; Zhang, Y.; Wu, C.; Zhuang, Y.; and Li, X. 2020 · 2010
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Prototypical networks for few-shot learning
Snell, J.; Swersky, K.; and Zemel, R. 2017 · 2017
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Federated Learning with Non-IID Data
Zhao, Y.; Li, M.; Lai, L.; Suda, N.; Civin, D.; and Chandra, V. 2018 · 2018
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SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning
Karimireddy, S. P.; Kale, S.; Mohri, M.; Reddi, S. J.; Stich, S. U.; and Suresh, A. T. 2019 · 2019
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CMFL: Mitigating communication overhead for federated learning
Luping, W.; Wei, W.; and Bo, L. 2019 · 2019
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Agnostic federated learning
Mohri, M.; Sivek, G.; and Suresh, A. T. 2019 · 2019
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Robust and communication-efficient federated learning from non-iid data
Sattler, F.; Wiedemann, S.; Müller, K.-R.; and Samek, W. 2019 · 2019
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A hybrid approach to privacy-preserving federated learning
Truex, S.; Baracaldo, N.; Anwar, A.; Steinke, T.; Ludwig, H.; Zhang, R.; and Zhou, Y. 2019 · 2019
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Wang, Z.; Song, M.; Zhang, Z.; Song, Y.; Wang, Q.; and Qi, H. 2019 · 2019
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Bayesian nonparametric federated learning of neural networks
Yurochkin, M.; Agarwal, M.; Ghosh, S.; Greenewald, K.; Hoang, N.; and Khazaeni, Y. 2019 · 2019
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Federated learning with hierarchical clustering of local updates to improve training on non-IID data
Briggs, C.; Fan, Z.; and Andras, P. 2020 · 2020
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A Simple Framework for Contrastive Learning of Visual Representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020 · 2020
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Distributionally Robust Federated Averaging
Fedvision: An online visual object detection platform powered by federated learning
Liu, Y.; Huang, A.; Luo, Y.; Huang, H.; Liu, Y.; Chen, Y.; Feng, L.; Chen, T.; Yu, H.; and Yang, Q. 2020 · 2020
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Robust Federated Learning: The Case of Affine Distribution Shifts
Reisizadeh, A.; Farnia, F.; Pedarsani, R.; and Jadbabaie, A. 2020 · 2020
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Personalized Federated Learning with Moreau Envelopes
T Dinh, C.; Tran, N.; and Nguyen, T. D. 2020 · 2020
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Towards federated unsupervised representation learning
van Berlo, B.; Saeed, A.; and Ozcelebi, T. 2020 · 2020
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Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition
Zhou, B.; Cui, Q.; Wei, X.-S.; and Chen, Z.-M. 2020 · 2020
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Deng, Y.; Kamani, M. M.; and Mahdavi, M. 2020 · 2020
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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Fallah, A.; Mokhtari, A.; and Ozdaglar, A. 2020 · 2020
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Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Hanzely, F.; Hanzely, S.; Horváth, S.; and Richtarik, P. 2020 · 2020
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Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
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Resource-efficient and convergence-preserving online participant selection in federated learning
Jin, Y.; Jiao, L.; Qian, Z.; Zhang, S.; Lu, S.; and Wang, X. 2020 · 2020
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Secure, privacy-preserving and federated machine learning in medical imaging
Kaissis, G. A.; Makowski, M. R.; Rückert, D.; and Braren, R. F. 2020 · 2020
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Supervised Contrastive Learning
Khosla, P.; Teterwak, P.; Wang, C.; Sarna, A.; Tian, Y.; Isola, P.; Maschinot, A.; Liu, C.; and Krishnan, D. 2020 · 2020
Cited alongside, same era.
Aggarwal, D.; Zhou, J.; and Jain, A. K. 2021 · 2021
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Evaluating the communication efficiency in federated learning algorithms
Asad, M.; Moustafa, A.; Ito, T.; and Aslam, M. 2021 · 2021
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Adaptive federated dropout: Improving communication efficiency and generalization for federated learning
Bouacida, N.; Hou, J.; Zang, H.; and Liu, X. 2021 · 2021
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Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging
Kumar, R.; Khan, A. A.; Kumar, J.; Zakria, A.; Golilarz, N. A.; Zhang, S.; Ting, Y.; Zheng, C.; and Wang, W. 2021 · 2021
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Model-Contrastive Federated Learning
Li, Q.; He, B.; and Song, D. 2021 · 2021
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Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
Wang, P.; Han, K.; Wei, X.-S.; Zhang, L.; and Wang, L. 2021 · 2021
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