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One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Learning methods for generic object recognition with invariance to pose and lighting
Yann LeCun, Fu Jie Huang, and Leon Bottou · 2004
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015
Sergey Ioffe and Christian Szegedy · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Deep models under the gan: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent, 2017
Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu · 2017
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Communication-efficient learning of deep networks from decentralized data, 2017
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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MODELDB: A system for machine learning model management
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Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Sergey Zagoruyko and Nikos Komodakis · 2017
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Federated learning with non-iid data, 2018
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Dafl: Data-free learning of student networks
Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, and Qi Tian · 2019
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Neel Guha, Ameet Talwalkar, and Virginia Smith · 2019
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Knowledge distillation with adversarial samples supporting decision boundary
Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi · 2019
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Supervae: Superpixelwise variational autoencoder for salient object detection
Bo Li, Zhengxing Sun, and Yuqi Guo · 2019
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Detecting robust co-saliency with recurrent co-attention neural network
Bo Li, Zhengxing Sun, Lv Tang, Yunhan Sun, and Jinlong Shi · 2019
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Daliang Li and Junpu Wang · 2019
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Ppgan: Privacy-preserving generative adversarial network
Yi Liu, Jialiang Peng, JQ James, and Yi Wu · 2019
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Bayesian nonparametric federated learning of neural networks, 2019
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni · 2019
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Fedbe: Making bayesian model ensemble applicable to federated learning, 2021
Hong-You Chen and Wei-Lun Chao · 2021
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Personalized retrogress-resilient framework for real-world medical federated learning
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Private hierarchical clustering in federated networks
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Federated learning on non-iid data silos: An experimental study, 2021
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What can be transferred: Unsupervised domain adaptation for endoscopic lesions segmentation
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M2m: Imbalanced classification via major-to-minor translation
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Practical one-shot federated learning for cross-silo setting
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Federated optimization in heterogeneous networks, 2020
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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FLAME: differentially private federated learning in the shuffle model
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