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Neural architecture search, which aims to automatically search for architectures (e.g., convolution, max pooling) of neural networks that maximize validation performance, has achieved remarkable progress recently.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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A statistical framework for differential privacy, 2008
Larry Wasserman and Shuheng Zhou · 2008
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A differentially private stochastic gradient descent algorithm for multiparty classification
Arun Rajkumar and Shivani Agarwal · 2012
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
Earlier work this paper cites.
Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
Cited alongside, same era.
Hierarchical representations for efficient architecture search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
cpsgd: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
Cited alongside, same era.
Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers · 2018
Cited alongside, same era.
Proxylessnas: Direct neural architecture search on target task and hardware
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
Later among the works it cites.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Later among the works it cites.
Deep learning with gaussian differential privacy
Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie J Su · 2019
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Jinshuo Dong, Aaron Roth, and Weijie J Su · 2019
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
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Han Cai, Ligeng Zhu, and Song Han · 2018
Cited alongside, same era.
The secret sharer: Measuring unintended neural network memorization & extracting secrets
Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, and Dawn Song · 2018
Cited alongside, same era.
Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
Cited alongside, same era.
Snas: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2018
Cited alongside, same era.
Later among the works it cites.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
Later among the works it cites.
Fednas: Federated deep learning via neural architecture search
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
Closest in time.
Neural architecture search over decentralized data
Mengwei Xu, Yuxin Zhao, Kaigui Bian, Gang Huang, Qiaozhu Mei, and Xuanzhe Liu · 2020
Closest in time.
Real-time federated evolutionary neural architecture search
Hangyu Zhu and Yaochu Jin · 2020
Closest in time.