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Existing federated learning paradigms usually extensively exchange distributed models at a central solver to achieve a more powerful model.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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First-and second-order methods for learning: between steepest descent and newton’s method
Roberto Battiti · 1992
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Mnist handwritten digit database, 2010
Yann LeCun, Corinna Cortes, and Chris Burges · 2010
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Data-free knowledge distillation for heterogeneous federated learning
Zhuangdi Zhu, Junyuan Hong, and Jiayu Zhou · 2010
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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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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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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Dataset distillation
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
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A survey on federated learning systems: vision, hype and reality for data privacy and protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He · 2019
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Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
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The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning
Giulia Denevi, Massimiliano Pontil, and Carlo Ciliberto · 2020
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Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen H Tran, and Tuan Dung Nguyen · 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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Federated knowledge distillation
Hyowoon Seo, Jihong Park, Seungeun Oh, Mehdi Bennis, and Seong-Lyun Kim · 2020
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Structured prediction for conditional meta-learning
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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Model-contrastive federated learning
Qinbin Li, Bingsheng He, and Dawn Song · 2021
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Node selection toward faster convergence for federated learning on non-iid data
Hongda Wu and Ping Wang · 2021
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Dataset Condensation with Differentiable Siamese Augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Dataset distillation by matching training trajectories
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Ruohan Wang, Yiannis Demiris, and Carlo Ciliberto · 2020
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Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers
Chi Zhang, Yujun Cai, Guosheng Lin, and Chunhua Shen · 2020
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Distilled one-shot federated learning
Yanlin Zhou, George Pu, Xiyao Ma, Xiaolin Li, and Dapeng Wu · 2020
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Deep leakage from gradients
Ligeng Zhu and Song Han · 2020
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Federated few-shot learning with adversarial learning
Chenyou Fan and Jianwei Huang · 2021
Cited alongside, same era.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
Cited alongside, same era.
Personalized cross-silo federated learning on non-iid data
Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, and Yong Zhang · 2021
Cited alongside, same era.
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu · 2022
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Acceleration of Federated Learning with Alleviated Forgetting in Local Training
Xu Chencheng, Hong Zhiwei, Huang Minlie, and Jiang Tao · 2022
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DisPFL: Towards communication-efficient personalized federated learning via decentralized sparse training
Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, and Dacheng Tao · 2022
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FedFOR: Stateless Heterogeneous Federated Learning with First-Order Regularization
Junjiao Tian, James Seale Smith, and Zsolt Kira · 2022
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Adaptive inertia: Disentangling the effects of adaptive learning rate and momentum
Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, and Masashi Sugiyama · 2022
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Fine-tuning global model via data-free knowledge distillation for non-iid federated learning
Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, and Ling-Yu Duan · 2022
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Dataset distillation using neural feature regression
Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba · 2022
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