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Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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A large-scale car dataset for fine-grained categorization and verification
Linjie Yang, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2017
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
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Mio-tcd: A new benchmark dataset for vehicle classification and localization
Zhiming Luo, Frederic Branchaud-Charron, Carl Lemaire, Janusz Konrad, Shaozi Li, Akshaya Mishra, Andrew Achkar, Justin Eichel, and Pierre-Marc Jodoin · 2018
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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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Data-free adversarial distillation
Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, and Mingli Song · 2019
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Hhhfl: Hierarchical heterogeneous horizontal federated learning for electroencephalography
Dashan Gao, Ce Ju, Xiguang Wei, Yang Liu, Tianjian Chen, and Qiang Yang · 2019
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Federated learning for ranking browser history suggestions
Florian Hartmann, Sunah Suh, Arkadiusz Komarzewski, Tim D Smith, and Ilana Segall · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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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 Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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Learn electronic health records by fully decentralized federated learning
Songtao Lu, Yawen Zhang, Yunlong Wang, and Christina Mack · 2019
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Communication-efficient federated learning for wireless edge intelligence in iot
Jed Mills, Jia Hu, and Geyong Min · 2019
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Zero-shot knowledge distillation in deep networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty · 2019
Cited alongside, same era.
Private federated learning with domain adaptation
Daniel Peterson, Pallika Kanani, and Virendra J Marathe · 2019
Cited alongside, same era.
Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
Cited alongside, same era.
From local sgd to local fixed-point methods for federated learning
Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, and Peter Richtarik · 2020
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Particle swarm optimized federated learning for industrial iot and smart city services
Basheer Qolomany, Kashif Ahmad, Ala Al-Fuqaha, and Junaid Qadir · 2020
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Model fusion via optimal transport
Sidak Pal Singh and Martin Jaggi · 2020
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Decentralised learning from independent multi-domain labels for person re-identification
Guile Wu and Shaogang Gong · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
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Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama · 2020
Cited alongside, same era.
Fedbe: Making bayesian model ensemble applicable to federated learning
Hong-You Chen and Wei-Lun Chao · 2020
Cited alongside, same era.
Optimal client sampling for federated learning
Wenlin Chen, Samuel Horvath, and Peter Richtarik · 2020
Cited alongside, same era.
Group knowledge transfer: Federated learning of large cnns at the edge
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
Cited alongside, same era.
Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, et al · 2020
Cited alongside, same era.
Stochastic client selection for federated learning with volatile clients
Tiansheng Huang, Weiwei Lin, Li Shen, Keqin Li, and Albert Y Zomaya · 2020
Cited alongside, same era.
Federated learning in smart city sensing: Challenges and opportunities
Ji Chu Jiang, Burak Kantarci, Sema Oktug, and Tolga Soyata · 2020
Cited alongside, same era.
Weiming Zhuang, Yonggang Wen, Xuesen Zhang, Xin Gan, Daiying Yin, Dongzhan Zhou, Shuai Zhang, and Shuai Yi · 2020
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Layer-peeled model: Toward understanding well-trained deep neural networks
Cong Fang, Hangfeng He, Qi Long, and Weijie J Su · 2021
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Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi · 2021
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Preservation of the global knowledge by not-true self knowledge distillation in federated learning
Gihun Lee, Yongjin Shin, Minchan Jeong, and Se-Young Yun · 2021
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Model-contrastive federated learning
Qinbin Li, Bingsheng He, and Dawn Song · 2021
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Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space
Quande Liu, Cheng Chen, Jing Qin, Qi Dou, and Pheng-Ann Heng · 2021
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Fedaux: Leveraging unlabeled auxiliary data in federated learning
Felix Sattler, Tim Korjakow, Roman Rischke, and Wojciech Samek · 2021
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A field guide to federated optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al · 2021
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Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges
Zhaohua Zheng, Yize Zhou, Yilong Sun, Zhang Wang, Boyi Liu, and Keqiu Li · 2021
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Data-free knowledge distillation for heterogeneous federated learning
Zhuangdi Zhu, Junyuan Hong, and Jiayu Zhou · 2021
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