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Communication and privacy are two critical concerns in distributed learning.
Federated learning: Challenges, methods, and future directions
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith · 1908
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Differentially private meta-learning
J. Li, M. Khodak, S. Caldas, and A. Talwalkar · 1909
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Randomized response: A survey technique for eliminating evasive answer bias
S. L. Warner · 1965
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The space complexity of approximating the frequency moments
N. Alon, Y. Matias, and M. Szegedy · 1996
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Finding frequent items in data streams
M. Charikar, K. Chen, and M. Farach-Colton · 2002
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An Improved Data Stream Summary: The Count-Min Sketch and Its Applications
G. Cormode and S. Muthukrishnan · 2005
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Efficient computation of frequent and top-k elements in data streams
A. Metwally, D. Agrawal, and A. E. Abbadi · 2005
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Removing camera shake from a single photograph
R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman · 2006
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Image and depth from a conventional camera with a coded aperture
A. Levin, R. Fergus, F. Durand, and W. T. Freeman · 2007
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Finding hierarchical heavy hitters in streaming data
G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava · 2008
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Differential privacy with compression
S. Zhou, K. Ligett, and L. Wasserman · 2009
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Sketch techniques for approximate query processing
G. Cormode · 2011
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Differential privacy
C. Dwork · 2011
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Local privacy and statistical minimax rates
J. C. Duchi, M. I. Jordan, and M. J. Wainwright · 2013
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
S. Ghadimi and G. Lan · 2013
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Software defined traffic measurement with opensketch
M. Yu, L. Jose, and R. Miao · 2013
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Gradient distribution priors for biomedical image processing
Y. Gong and I. F. Sbalzarini · 2014
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Federated learning: strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
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One sketch to rule them all: Rethinking network flow monitoring with univmon
Z. Liu, A. Manousis, G. Vorsanger, V. Sekar, and V. Braverman · 2016
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Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Sanjabi, M. Zaheer, A. Talwalkar, and V. Smith · 2018
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally · 2018
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Learning differentially private recurrent language models
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang · 2018
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Total stochastic gradient algorithms and applications in reinforcement learning
P. Parmas · 2018
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Cocoa: a general framework for communication-efficient distributed optimization
V. Smith, S. Forte, C. Ma, M. Takac, M. I. Jordan, and M. Jaggi · 2018
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Efficient private statistics with succinct sketches
L. Melis, G. Danezis, and E. D. Cristofaro · 2016
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Aide: Fast and communication efficient distributed optimization
S. J. Reddi, J. Konečnỳ, P. Richtárik, B. Póczós, and A. Smola · 2016
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Randomized requantization with local differential privacy
S. Xiong, A. D. Sarwate, and N. B. Mandayam · 2016
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Practical locally private heavy hitters
R. Bassily, K. Nissim, U. Stemmer, and A. G. Thakurta · 2017
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Practical secure aggregation for privacy-preserving machine learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2017
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
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Federated multi-task learning
V. Smith, C.-K. Chiang, M. Sanjabi, and A. Talwalkar · 2017
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K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecny, S. Mazzocchi, H. B. McMahan, T. V. Overveldt, D. Petrou, D. Ramage, and J. Roselander · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks
N. Carlini, C. Liu, Ú. Erlingsson, J. Kos, and D. Song · 2019
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Secure computation for machine learning with spdz
V. Chen, V. Pastro, and M. Raykova · 2019
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Lower bounds for locally private estimation via communication complexity
J. Duchi and R. Rogers · 2019
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Scalable and differentially private distributed aggregation in the shuffled model
B. Ghazi, R. Pagh, and A. Velingker · 2019
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Communication-efficient distributed sgd with sketching
N. Ivkin, D. Rothchild, E. Ullah, V. Braverman, I. Stoica, and R. Arora · 2019
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Compressing gradient optimizers via count-sketches
R. Spring, A. Kyrillidis, V. Mohan, and A. Shrivastava · 2019
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Local sgd converges fast and communicates little
S. U. Stich · 2019
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Federated heavy hitters discovery with differential privacy
W. Zhu, P. Kairouz, H. Sun, B. McMahan, and W. Li · 2019
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