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Standard Federated Learning (FL) techniques are limited to clients with identical network architectures.
Hyper-sphere quantization: Communication-efficient SGD for federated learning
Xinyan Dai, Xiao Yan, Kaiwen Zhou, Han Yang, Kelvin Kai Wing Ng, James Cheng, and Yu Fan · 1911
Earlier work this paper cites.
Federated learning of a mixture of global and local models
Filip Hanzely and Peter Richtárik · 2002
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CIFAR-10 (canadian institute for advanced research), 2009
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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A dynamic convolutional layer for short range weather prediction
Benjamin Klein, Lior Wolf, and Yehuda Afek · 2015
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Federated optimization:distributed optimization beyond the datacenter, 2015
Jakub Konečný, Brendan McMahan, and Daniel Ramage · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Resnet in resnet: Generalizing residual architectures
Sasha Targ, Diogo Almeida, and Kevin Lyman · 2016
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Hypernetworks
David Ha, Andrew M. Dai, and Quoc V. Le · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Federated learning: Strategies for improving communication efficiency, 2017
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2017
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Gated graph sequence neural networks, 2017
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Character-level language modeling with recurrent highway hypernetworks
Joseph Suarez · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald Summers · 2017
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cpsgd: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
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SMASH: One-shot model architecture search through hypernetworks
Andrew Brock, Theo Lim, J.M. Ritchie, and Nick Weston · 2018
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Inductive representation learning on large graphs, 2018
William L. Hamilton, Rex Ying, and Jure Leskovec · 2018
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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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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
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Survey of personalization techniques for federated learning
Viraj Kulkarni, Milind Kulkarni, and Aniruddha Pant · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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Ensemble distillation for robust model fusion in federated learning, 2020
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H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
Cited alongside, same era.
Principled weight initialization for hypernetworks
Oscar Chang, Lampros Flokas, and Hod Lipson · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Measuring the effects of non-identical data distribution for federated visual classification, 2019
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Privacy-preserving federated brain tumour segmentation
Wenqi Li, Fausto Milletarì, Daguang Xu, Nicola Rieke, Jonny Hancox, Wentao Zhu, Maximilian Baust, Yan Cheng, Sébastien Ourselin, M Jorge Cardoso, et al · 2019
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Deep meta functionals for shape representation
Gidi Littwin and Lior Wolf · 2019
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Tao Lin, Lingjing Kong, Sebastian U. Stich, and Martin Jaggi · 2020
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Molecule property prediction and classification with graph hypernetworks
Eliya Nachmani and Lior Wolf · 2020
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Graph hypernetworks for neural architecture search, 2020
Chris Zhang, Mengye Ren, and Raquel Urtasun · 2020
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Federated heavy hitters discovery with differential privacy
Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, and Wei Li · 2020
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Communication-efficient federated learning
Mingzhe Chen, Nir Shlezinger, H Vincent Poor, Yonina C Eldar, and Shuguang Cui · 2021
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Hetero{fl}: Computation and communication efficient federated learning for heterogeneous clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2021
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Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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Parameter prediction for unseen deep architectures
Boris Knyazev, Michal Drozdzal, Graham W Taylor, and Adriana Romero-Soriano · 2021
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A survey on security and privacy of federated learning
Viraaji Mothukuri, Reza M Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava · 2021
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Personalized federated learning using hypernetworks
Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik · 2021
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From local structures to size generalization in graph neural networks, 2021
Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, and Haggai Maron · 2021
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Personalized federated learning with first order model optimization
Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, and Jose M. Alvarez · 2021
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