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Federated Learning (FL) is a machine learning setting where many devices collaboratively train a machine learning model while keeping the training data decentralized.
Learning with ensembles: How overfitting can be useful
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L. I. Kuncheva and C. J. Whitaker · 2003
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Model compression
C. Buciluǎ, R. Caruana, and A. Niculescu-Mizil · 2006
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Domain adaptation with multiple sources
Y. Mansour, M. Mohri, and A. Rostamizadeh · 2009
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A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
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Neural networks for machine learning, 2012
G. Hinton · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation, 2013
Y. Bengio, N. Léonard, and A. Courville · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Understanding machine learning: From theory to algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2015
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Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
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Character-level convolutional networks for text classification
X. Zhang, J. Zhao, and Y. LeCun · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Binarized neural networks
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, et al · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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S. Zagoruyko and N. Komodakis · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
P. Chrabaszcz, I. Loshchilov, and F. Hutter · 2017
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Differentially private federated learning: A client level perspective
R. C. Geyer, T. Klein, and M. Nabi · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Like what you like: Knowledge distill via neuron selectivity transfer
Z. Huang and N. Wang · 2017
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Quantized neural networks: Training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2017
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Federated multi-task learning
V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar · 2017
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Learning from multiple teacher networks
S. You, C. Xu, C. Xu, and D. Tao · 2017
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Large scale distributed neural network training through online distillation
R. Anil, G. Pereyra, A. Passos, R. Ormandi, G. E. Dahl, and G. E. Hinton · 2018
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Leaf: A benchmark for federated settings
S. Caldas, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Knowledge flow: Improve upon your teachers
I.-J. Liu, J. Peng, and A. G. Schwing · 2019
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A simple baseline for bayesian uncertainty in deep learning
W. J. Maddox, P. Izmailov, T. Garipov, D. P. Vetrov, and A. G. Wilson · 2019
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Zero-shot knowledge transfer via adversarial belief matching
P. Micaelli and A. J. Storkey · 2019
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M. Mohri, G. Sivek, and A. T. Suresh · 2019
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Zero-shot knowledge distillation in deep networks
G. K. Nayak, K. R. Mopuri, V. Shaj, R. V. Babu, and A. Chakraborty · 2019
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T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar · 2018
Cited alongside, same era.
Algorithms and theory for multiple-source adaptation
J. Hoffman, M. Mohri, and N. Zhang · 2018
Cited alongside, same era.
Averaging weights leads to wider optima and better generalization
P. Izmailov, D. Podoprikhin, T. Garipov, D. Vetrov, and A. G. Wilson · 2018
Cited alongside, same era.
E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, and S.-L. Kim · 2018
Cited alongside, same era.
Paraphrasing complex network: Network compression via factor transfer
J. Kim, S. Park, and N. Kwak · 2018
Cited alongside, same era.
Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2018
Cited alongside, same era.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
N. Ma, X. Zhang, H.-T. Zheng, and J. Sun · 2018
Cited alongside, same era.
S. Park and N. Kwak · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
V. Sanh, L. Debut, J. Chaumond, and T. Wolf · 2019
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Overcoming forgetting in federated learning on non-iid data
N. Shoham, T. Avidor, A. Keren, N. Israel, D. Benditkis, L. Mor-Yosef, and I. Zeitak · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. V. Le · 2019
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Contrastive representation distillation
Y. Tian, D. Krishnan, and P. Isola · 2019
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Similarity-preserving knowledge distillation
F. Tung and G. Mori · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman · 2019
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Distilled person re-identification: Towards a more scalable system
A. Wu, W. Zheng, X. Guo, and J. Lai · 2019
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Bayesian nonparametric federated learning of neural networks
M. Yurochkin, M. Agarwal, S. Ghosh, K. Greenewald, T. N. Hoang, and Y. Khazaeni · 2019
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Feddistill: Making bayesian model ensemble applicable to federated learning
H.-Y. Chen and W.-L. Chao · 2020
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Adaptive personalized federated learning
Y. Deng, M. M. Kamani, and M. Mahdavi · 2020
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Group knowledge transfer: Collaborative training of large cnns on the edge
C. He, S. Avestimehr, and M. Annavaram · 2020
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Federated visual classification with real-world data distribution
T.-M. H. Hsu, H. Qi, and M. Brown · 2020
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Fair resource allocation in federated learning
T. Li, M. Sanjabi, A. Beirami, and V. Smith · 2020
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Dynamic model pruning with feedback
T. Lin, S. U. Stich, L. Barba, D. Dmitriev, and M. Jaggi · 2020
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Don’t use large mini-batches, use local SGD
T. Lin, S. U. Stich, K. K. Patel, and M. Jaggi · 2020
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Distributed gradient methods for convex machine learning problems in networks: Distributed optimization
A. Nedic · 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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Model fusion via optimal transport
S. P. Singh and M. Jaggi · 2020
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Federated model distillation with noise-free differential privacy
L. Sun and L. Lyu · 2020
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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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Distilled one-shot federated learning
Y. Zhou, G. Pu, X. Ma, X. Li, and D. Wu · 2020
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