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In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al. 2019 · 1902
Earlier work this paper cites.
How to generate and exchange secrets
A. C. Yao. 1986 · 1986
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Exponentially small bounds on the expected optimum of the partition and subset sum problems
George S Lueker. 1998 · 1998
Earlier work this paper cites.
Differential privacy: A survey of results
Cynthia Dwork. 2008 · 2008
Earlier work this paper cites.
Esmfl: Efficient and secure models for federated learning
Sheng Lin, Chenghong Wang, Hongjia Li, Jieren Deng, Yanzhi Wang, and Caiwen Ding. 2020 · 2009
Earlier work this paper cites.
Sapag: a self-adaptive privacy attack from gradients
Yijue Wang, Jieren Deng, Dan Guo, Chenghong Wang, Xianrui Meng, Hang Liu, Caiwen Ding, and Sanguthevar Rajasekaran. 2020 · 2009
Earlier work this paper cites.
Additively homomorphic encryption with a double decryption mechanism, revisited
Andreas Peter, Max Kronberg, Wilke Trei, and Stefan Katzenbeisser. 2012 · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al. 2014 · 2014
Earlier work this paper cites.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016
Cited alongside, same era.
Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017 · 2017
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Circnn: Accelerating and compressing deep neural networks using block-circulant weight matrices
Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo, Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, Xiaolong Ma, Yipeng Zhang, Jian Tang, Qinru Qiu, Xue Lin, and Bo Yuan. 2017 · 2017
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han. 2019 · 2019
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Pconv: The missing but desirable sparsity in dnn weight pruning for real-time execution on mobile devices
Xiaolong Ma, Fu-Ming Guo, Wei Niu, Xue Lin, Jian Tang, Kaisheng Ma, Bin Ren, and Yanzhi Wang. 2020 · 2020
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Darb: A density-adaptive regular-block pruning for deep neural networks
Ao Ren, Tao Zhang, Yuhao Wang, Sheng Lin, Peiyan Dong, Yen-Kuang Chen, Yuan Xie, and Yanzhi Wang. 2020 · 2020
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Findings of the Association for Computational Linguistics: EMNLP 2021 . Association for Computational Linguistics
Jieren Deng, Yijue Wang, Ji Li, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, and Caiwen Ding, editors. 2021 · 2021
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Enabling retrain-free deep neural network pruning using surrogate lagrangian relaxation
Deniz Gurevin, Mikhail Bragin, Caiwen Ding, Shanglin Zhou, Lynn Pepin, Bingbing Li, and Fei Miao. 2021 · 2021
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Theoretical properties for neural networks with weight matrices of low displacement rank
Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang, and Bo Yuan. 2017 · 2017
Cited alongside, same era.
Scalable private learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson. 2018 · 2018
Cited alongside, same era.
Model compression with adversarial robustness: A unified optimization framework
Shupeng Gui, Haotao N Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, and Ji Liu. 2019 · 2019
Cited alongside, same era.
A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou. 2019 · 2019
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017a
Cited in the paper.
Communication-efficient learning of deep networks from decentralized data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017b
Cited in the paper.
Against membership inference attack: Pruning is all you need
Yijue Wang, Chenghong Wang, Zigeng Wang, Shanglin Zhou, Hang Liu, Jinbo Bi, Caiwen Ding, and Sanguthevar Rajasekaran. 2021 · 2021
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A novel privacy-preserving federated genome-wide association study framework and its application in identifying potential risk variants in ankylosing spondylitis
Xin Wu, Hao Zheng, Zuochao Dou, Feng Chen, Jieren Deng, Xiang Chen, Shengqian Xu, Guanmin Gao, Mengmeng Li, Zhen Wang, et al. 2021 · 2021
Later among the works it cites.
Tinyadc: Peripheral circuit-aware weight pruning framework for mixed-signal dnn accelerators
Geng Yuan, Payman Behnam, Yuxuan Cai, Ali Shafiee, Jingyan Fu, Zhiheng Liao, Zhengang Li, Xiaolong Ma, Jieren Deng, Jinhui Wang, Mahdi Bojnordi, Yanzhi Wang, and Caiwen Ding. 2021 · 2021
Later among the works it cites.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. 2016 · 2082
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