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Providing privacy protection has been one of the primary motivations of Federated Learning (FL).
Calibrating Noise to Sensitivity in Private Data Analysis
Cynthia Dwork, F. McSherry, K. Nissim, and A. Smith, · 2006
Earlier work this paper cites.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky, Geoffrey Hinton, et al., · 2009
Earlier work this paper cites.
“Imagenet classification with deep convolutional neural networks,”
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, · 2012
Earlier work this paper cites.
“Private empirical risk minimization: Efficient algorithms and tight error bounds,”
Raef Bassily, Adam Smith, and Abhradeep Thakurta, · 2014
Earlier work this paper cites.
“The algorithmic foundations of differential privacy.,”
Cynthia Dwork and Aaron Roth, · 2014
Earlier work this paper cites.
“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.
“Deep learning with differential privacy,”
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang, · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Differentially private federated learning: A client level perspective,”
Robin C Geyer, Tassilo Klein, and Moin Nabi, · 2017
Earlier work this paper cites.
“EMNIST: Extending MNIST to handwritten letters,”
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik, · 2017
Earlier work this paper cites.
“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
Earlier work this paper cites.
“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
Cited alongside, same era.
“Mobilenetv2: Inverted residuals and linear bottlenecks,”
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen, · 2018
Cited alongside, same era.
“Variance reduced local sgd with lower communication complexity,”
Xianfeng Liang, Shuheng Shen, Jingchang Liu, Zhen Pan, Enhong Chen, and Yifei Cheng, · 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
Cited alongside, same era.
“Federated learning with bayesian differential privacy,”
Aleksei Triastcyn and Boi Faltings, · 2019
Cited alongside, same era.
“A framework for evaluating gradient leakage attacks in federated learning,”
Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu, · 2020
Later among the works it cites.
“LDP-Fed: Federated learning with local differential privacy,”
Stacey Truex, Ling Liu, Ka-Ho Chow, Mehmet Emre Gursoy, and Wenqi Wei, · 2020
Later among the works it cites.
“D2p-fed: Differentially private federated learning with efficient communication,”
Lun Wang, Ruoxi Jia, and Dawn Song, · 2020
Later among the works it cites.
“Understanding gradient clipping in private sgd: A geometric perspective,”
Xiangyi Chen, Steven Z Wu, and Mingyi Hong, · 2020
Later among the works it cites.
“Federated learning with differential privacy: Algorithms and performance analysis,”
Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H Yang, Farhad Farokhi, Shi Jin, Tony QS Quek, and H Vincent Poor, · 2020
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“First analysis of local gd on heterogeneous data,”
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik, · 2019
Cited alongside, same era.
“Differential privacy-enabled federated learning for sensitive health data,”
Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, and Amar Das, · 2019
Cited alongside, same era.
“Scaffold: Stochastic controlled averaging for federated learning,”
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh, · 2020
Cited alongside, same era.
“FedPD: A federated learning framework with optimal rates and adaptivity to Non-IID data,” 2020
Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, and Yang Liu, · 2020
Cited alongside, same era.
“idlg: Improved deep leakage from gradients,”
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen, · 2020
Cited alongside, same era.
“Deep leakage from gradients,”
Ligeng Zhu and Song Han, · 2020
Cited alongside, same era.
Later among the works it cites.
“Characterizing private clipped gradient descent on convex generalized linear problems,”
Shuang Song, Om Thakkar, and Abhradeep Thakurta, · 2020
Later among the works it cites.
“Estimating full lipschitz constants of deep neural networks,”
Calypso Herrera, Florian Krach, and Josef Teichmann, · 2020
Later among the works it cites.
“Evading the curse of dimensionality in unconstrained private glms,”
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta, · 2021
Closest in time.
“Achieving linear speedup with partial worker participation in Non-IID federated learning,”
Haibo Yang, Minghong Fang, and Jia Liu, · 2021
Closest in time.
“Adaptive federated optimization,”
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan, · 2021
Closest in time.