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Today data is often scattered among billions of resource-constrained edge devices with security and privacy constraints.
Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 1905
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Abnormal client behavior detection in federated learning
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Group knowledge transfer: Federated learning of large cnns at the edge
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2007
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Fedml: A research library and benchmark for federated machine learning
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Adam: A method for stochastic optimization
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Song Han, Huizi Mao, and William J Dally · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Mastering the game of go with deep neural networks and tree search
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Communication-efficient learning of deep networks from decentralized data
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Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
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Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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Learning from multiple teacher networks
Shan You, Chang Xu, Chao Xu, and Dacheng Tao · 2017
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Federated learning of predictive models from federated electronic health records
Theodora S Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch Paschalidis, and Wei Shi · 2018
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Leaf: A benchmark for federated settings
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Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
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An exponential learning rate schedule for deep learning
Zhiyuan Li and Sanjeev Arora · 2019
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2019
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Federated learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu · 2019
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Bayesian nonparametric federated learning of neural networks
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Epa-cppa: An efficient, provably-secure and anonymous conditional privacy-preserving authentication scheme for vehicular ad hoc networks
JiLiang Li, Kim-Kwang Raymond Choo, WeiGuo Zhang, Saru Kumari, Joel JPC Rodrigues, Muhammad Khurram Khan, and Dieter Hogrefe · 2018
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Don’t use large mini-batches, use local sgd
Tao Lin, Sebastian U Stich, Kumar Kshitij Patel, and Martin Jaggi · 2018
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Netadapt: Platform-aware neural network adaptation for mobile applications
Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, and Hartwig Adam · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
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A little is enough: Circumventing defenses for distributed learning
Moran Baruch, Gilad Baruch, and Yoav Goldberg · 2019
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
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Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer
Hongyan Chang, Virat Shejwalkar, Reza Shokri, and Amir Houmansadr · 2019
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Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Sohei Itahara, Takayuki Nishio, Yusuke Koda, Masahiro Morikura, and Koji Yamamoto · 2020
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Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi · 2020
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Improved protein structure prediction using potentials from deep learning
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, et al · 2020
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Not all knowledge is created equal
Ziyun Li, Xinshao Wang, Haojin Yang, Di Hu, Neil Martin Robertson, David A. Clifton, and Christoph Meinel · 2021
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Knowledge inheritance for pre-trained language models
Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, et al · 2021
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Selective knowledge distillation for neural machine translation
Fusheng Wang, Jianhao Yan, Fandong Meng, and Jie Zhou · 2021
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Reinforced multi-teacher selection for knowledge distillation
Fei Yuan, Linjun Shou, Jian Pei, Wutao Lin, Ming Gong, Yan Fu, and Daxin Jiang · 2021
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