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Federated learning (FL) is a machine learning setting where many clients (e.g.
SecureBoost: A lossless federated learning framework
Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang · 1901
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Peer-to-peer Federated Learning on Graphs
Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi, and Farinaz Koushanfar · 1901
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Towards federated learning at scale: System design
K. A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé M Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 1902
Earlier work this paper cites.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 1902
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Federated learning of out-of-vocabulary words
Mingqing Chen, Rajiv Mathews, Tom Ouyang, and Françoise Beaufays · 1903
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Data poisoning against differentially-private learners: Attacks and defenses
Yuzhe Ma, Xiaojin Zhu, and Justin Hsu · 1903
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Semi-cyclic stochastic gradient descent
Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, and Kunal Talwar · 1904
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan and Vitaly Shmatikov · 1905
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Fair decision making using privacy-protected data
Satya Kuppam, Ryan McKenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 1905
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Decentralized Bayesian learning over graphs
Anusha Lalitha, Xinghan Wang, Osman Kilinc, Yongxi Lu, Tara Javidi, and Farinaz Koushanfar · 1905
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Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, and Virginia Smith · 1905
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MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling
Jianyu Wang, Anit Sahu, Gauri Joshi, and Soummya Kar · 1905
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Scalable and differentially private distributed aggregation in the shuffled model
Badih Ghazi, Rasmus Pagh, and Ameya Velingker · 1906
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On the convergence of FedAvg on non-IID data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 1907
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Robust and communication-efficient collaborative learning
Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, and Ramtin Pedarsani · 1907
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On the power of multiple anonymous messages
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, and Ameya Velingker · 1908
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Testing robustness against unforeseen adversaries
Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, and Jacob Steinhardt · 1908
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First analysis of local GD on heterogeneous data, 2019a
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 1909
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Better communication complexity for local SGD, 2019b
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 1909
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Differentially private meta-learning
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, and Ameet Talwalkar · 1909
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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 1909
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The non-IID data quagmire of decentralized machine learning, 2019
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B. Gibbons · 1910
Earlier work this paper cites.
Communication efficient decentralized training with multiple local updates
Xiang Li, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 1910
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SlowMo: Improving communication-efficient distributed SGD with slow momentum
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, and Michael Rabbat · 1910
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Federated evaluation of on-device personalization
Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, and Daniel Ramage · 1910
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Generative models for effective ML on private, decentralized datasets, 2019
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, and Blaise Aguera y Arcas · 1911
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Local adaalter: Communication-efficient stochastic gradient descent with adaptive learning rates
Cong Xie, Oluwasanmi Koyejo, Indranil Gupta, and Haibin Lin · 1911
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The power of synergy in differential privacy: Combining a small curator with local randomizers
Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, and Uri Stemmer · 1912
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A communication efficient vertical federated learning framework
Yang Liu, Yan Kang, Xinwei Zhang, Liping Li, Yong Cheng, Tianjian Chen, Mingyi Hong, and Qiang Yang · 1912
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Randomized response: A survey technique for eliminating evasive answer bias
Stanley L. Warner · 1965
Earlier work this paper cites.
On data banks and privacy homomorphisms
Ronald L Rivest, Len Adleman, and Michael L Dertouzos · 1978
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Untraceable electronic mail, return addresses, and digital pseudonyms
David Chaum · 1981
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The Byzantine generals problem
Leslie Lamport, Robert Shostak, and Marshall Pease · 1982
Earlier work this paper cites.
Protocols for secure computations
Andrew C Yao · 1982
Earlier work this paper cites.
How to generate and exchange secrets (extended abstract)
Andrew Chi-Chih Yao · 1986
Earlier work this paper cites.
How to play any mental game
O. Goldreich, S. Micali, and A. Wigderson · 1987
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The knowledge complexity of interactive proof systems
Shafi Goldwasser, Silvio Micali, and Charles Rackoff · 1989
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Efficient identification and signatures for smart cards
Claus P. Schnorr · 1990
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Checking computations in polylogarithmic time
László Babai, Lance Fortnow, Leonid A. Levin, and Mario Szegedy · 1991
Earlier work this paper cites.
Post-stratification: A modeler’s perspective
R. J. A. Little · 1993
Earlier work this paper cites.
Statistical aspects of neural networks
Brian D Ripley · 1993
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StatLog: comparison of classification algorithms on large real-world problems
Ross D. King, Cao Feng, and Alistair Sutherland · 1995
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Automatic parameter selection by minimizing estimated error
Ron Kohavi and George H John · 1995
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Communication Complexity
Eyal Kushilevitz and Noam Nisan · 1997
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Replication is not needed: Single database, computationally-private information retrieval
Eyal Kushilevitz and Rafail Ostrovsky · 1997
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Private information retrieval
Benny Chor, Eyal Kushilevitz, Oded Goldreich, and Madhu Sudan · 1998
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Privacy-preserving data mining
Rakesh Agrawal and Ramakrishnan Srikant · 2000
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A model of inductive bias learning
Jonathan Baxter · 2000
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Computationally sound proofs
Silvio Micali · 2000
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Efficient asynchronous secure multiparty distributed computation
K Srinathan and C Pandu Rangan · 2000
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The sybil attack
John R. Douceur · 2002
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Three approaches for personalization with applications to federated learning
Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh · 2002
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Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
Jinhyun So, Basak Guler, and A Salman Avestimehr · 2002
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Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov · 2002
Earlier work this paper cites.
HIPAA regulations-a new era of medical-record privacy?
George J Annas · 2003
Earlier work this paper cites.
Extending oblivious transfers efficiently
Yuval Ishai, Joe Kilian, Kobbi Nissim, and Erez Petrank · 2003
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Tor: The second-generation onion router
Roger Dingledine, Nick Mathewson, and Paul Syverson · 2004
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rtop-k: A statistical estimation approach to distributed sgd
Leighton Pate Barnes, Huseyin A. Inan, Berivan Isik, and Ayfer Ozgur · 2005
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A geometric approach to information-theoretic private information retrieval
D. Woodruff and S. Yekhanin · 2005
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Randomized gossip algorithms
Stephen Boyd, Arpita Ghosh, Balaji Prabhakar, and Devavrat Shah · 2006
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Backdoor attacks on federated meta-learning
Chien-Lun Chen, Leana Golubchik, and Marco Paolieri · 2006
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A lattice-based computationally-efficient private information retrieval protocol
Carlos Aguilar-Melchor and Philippe Gaborit · 2007
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A theory of multiple-source adaptation with limited target labeled data
Yishay Mansour, Mehryar Mohri, Ananda Theertha Suresh, and Ke Wu · 2007
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Pioneer: Verifying code integrity and enforcing untampered code execution on legacy systems
Arvind Seshadri, Mark Luk, Adrian Perrig, Leendert van Doom, and Pradeep K. Khosla · 2007
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On the computational practicality of private information retrieval
Radu Sion and Bogdan Carbunar · 2007
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Measuring and testing dependence by correlation of distances
Gábor J Székely, Maria L Rizzo, Nail K Bakirov, et al · 2007
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Casting out demons: Sanitizing training data for anomaly sensors
Gabriela F Cretu, Angelos Stavrou, Michael E Locasto, Salvatore J Stolfo, and Angelos D Keromytis · 2008
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Delegating computation: interactive proofs for muggles
Shafi Goldwasser, Yael Tauman Kalai, and Guy N. Rothblum · 2008
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Mime: Mimicking centralized stochastic algorithms in federated learning
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2008
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Privacy-preserving SVM classification
Jaideep Vaidya, Hwanjo Yu, and Xiaoqian Jiang · 2008
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Secure multiparty computation goes live
Peter Bogetoft, Dan Lund Christensen, Ivan Damgård, Martin Geisler, Thomas P. Jakobsen, Mikkel Krøigaard, Janus Dam Nielsen, Jesper Buus Nielsen, Kurt Nielsen, Jakob Pagter, Michael I. Schwartzbach, and Tomas Toft · 2009
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Fully homomorphic encryption using ideal lattices
Craig Gentry · 2009
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Universally utility-maximizing privacy mechanisms
Arpita Ghosh, Tim Roughgarden, and Mukund Sundararajan · 2009
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Computational differential privacy
Ilya Mironov, Omkant Pandey, Omer Reingold, and Salil Vadhan · 2009
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Dataset Shift in Machine Learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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SEPIA: Privacy-preserving aggregation of multi-domain network events and statistics
Martin Burkhart, Mario Strasser, Dilip Many, and Xenofontas Dimitropoulos · 2010
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Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil Vadhan · 2010
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Non-interactive verifiable computing: Outsourcing computation to untrusted workers
Rosario Gennaro, Craig Gentry, and Bryan Parno · 2010
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Adaptive bound optimization for online convex optimization
H Brendan McMahan and Matthew Streeter · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Differentially private aggregation of distributed time-series with transformation and encryption
Vibhor Rastogi and Suman Nath · 2010
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I have a DREAM!: DIfferentially PrivatE smart Metering
Gergely Ács and Claude Castelluccia · 2011
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Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Better mini-batch algorithms via accelerated gradient methods
Andrew Cotter, Ohad Shamir, Nati Srebro, and Karthik Sridharan · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Differential privacy under fire
Andreas Haeberlen, Benjamin C Pierce, and Arjun Narayan · 2011
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Secure computation on the web: Computing without simultaneous interaction
Shai Halevi, Yehuda Lindell, and Benny Pinkas · 2011
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Fairness-aware learning through regularization approach
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma · 2011
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith · 2011
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Revisiting the computational practicality of private information retrieval
Femi Olumofin and Ian Goldberg · 2011
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Parallel random numbers: As easy as 1, 2, 3
John K Salmon, Mark A Moraes, Ron O Dror, and David E Shaw · 2011
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A novel error-tolerant anonymous linking code
R. Schnell, T. Bachteler, and J. Reiher · 2011
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Privacy-preserving aggregation of time-series data
Elaine Shi, HTH Chan, Eleanor Rieffel, Richard Chow, and Dawn Song · 2011
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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From extractable collision resistance to succinct non-interactive arguments of knowledge, and back again
Nir Bitansky, Ran Canetti, Alessandro Chiesa, and Eran Tromer · 2012
Earlier work this paper cites.
Deploying secure multi-party computation for financial data analysis - (short paper)
Dan Bogdanov, Riivo Talviste, and Jan Willemson · 2012
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Fully homomorphic encryption without modulus switching from classical gapsvp
Zvika Brakerski · 2012
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(leveled) fully homomorphic encryption without bootstrapping
Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan · 2012
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Privacy-preserving stream aggregation with fault tolerance
T-H Hubert Chan, Elaine Shi, and Dawn Song · 2012
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Data matching: concepts and techniques for record linkage, entity resolution, and duplicate detection
P. Christen · 2012
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Elements of information theory
Thomas M Cover and Joy A Thomas · 2012
Earlier work this paper cites.
Large scale distributed deep networks
Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc’Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, and Andrew Y. Ng · 2012
Earlier work this paper cites.
Optimal distributed online prediction using mini-batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao · 2012
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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SMART: secure and minimal architecture for (establishing dynamic) root of trust
Karim Eldefrawy, Gene Tsudik, Aurélien Francillon, and Daniele Perito · 2012
Earlier work this paper cites.
Somewhat practical fully homomorphic encryption
Junfeng Fan and Frederik Vercauteren · 2012
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Architecture instruction set extensions programming reference
R Intel · 2012
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An optimal method for stochastic composite optimization
Guanghui Lan · 2012
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On significance of the least significant bits for differential privacy
Ilya Mironov · 2012
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Learning to label aerial images from noisy data
Volodymyr Mnih and Geoffrey E Hinton · 2012
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A unifying view on dataset shift in classification
Jose G. Moreno-Torres, Troy Raeder, RocíO Alaiz-RodríGuez, Nitesh V. Chawla, and Francisco Herrera · 2012
Earlier work this paper cites.
Defense in depth: A practical strategy for achieving Information Assurance in today’s highly networked environments
NSA · 2012
Earlier work this paper cites.
Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
Earlier work this paper cites.
Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
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Quadratic span programs and succinct NIZKs without PCPs
Rosario Gennaro, Craig Gentry, Bryan Parno, and Mariana Raykova · 2013
Earlier work this paper cites.
Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
Earlier work this paper cites.
Privacy-preserving ridge regression on hundreds of millions of records
Valeria Nikolaenko, Udi Weinsberg, Stratis Ioannidis, Marc Joye, Dan Boneh, and Nina Taft · 2013
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Efficient private record linkage of very large datasets
R. Schnell · 2013
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Lifelong machine learning systems: Beyond learning algorithms
Daniel L. Silver, Qiang Yang, and Lianghao Li · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Information-theoretic lower bounds for distributed statistical estimation with communication constraints
Yuchen Zhang, John Duchi, Micheal I. Jordan, and Martin J. Wainwright · 2013
Earlier work this paper cites.
Zerocash: Decentralized anonymous payments from bitcoin
Eli Ben-Sasson, Alessandro Chiesa, Christina Garman, Matthew Green, Ian Miers, Eran Tromer, and Madars Virza · 2014
Earlier work this paper cites.
On key recovery attacks against existing somewhat homomorphic encryption schemes
Massimo Chenal and Qiang Tang · 2014
Earlier work this paper cites.
Scale-invariant fully homomorphic encryption over the integers
Jean-Sébastien Coron, Tancrède Lepoint, and Mehdi Tibouchi · 2014
Earlier work this paper cites.
Domain adaptation and sample bias correction theory and algorithm for regression
Corinna Cortes and Mehryar Mohri · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
Earlier work this paper cites.
A minimalist approach to remote attestation
Aurélien Francillon, Quan Nguyen, Kasper Bonne Rasmussen, and Gene Tsudik · 2014
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Extremal mechanisms for local differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2014
Earlier work this paper cites.
Pufferfish: A framework for mathematical privacy definitions
Daniel Kifer and Ashwin Machanavajjhala · 2014
Earlier work this paper cites.
TrustLite: a security architecture for tiny embedded devices
Patrick Koeberl, Steffen Schulz, Ahmad-Reza Sadeghi, and Vijay Varadharajan · 2014
Earlier work this paper cites.
Ethereum: A secure decentralised generalised transaction ledger
Gavin Wood et al · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Local, private, efficient protocols for succinct histograms
Raef Bassily and Adam Smith · 2015
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Geppetto: Versatile verifiable computation
Craig Costello, Cédric Fournet, Jon Howell, Markulf Kohlweiss, Benjamin Kreuter, Michael Naehrig, Bryan Parno, and Samee Zahur · 2015
Earlier work this paper cites.
BinaryConnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
A comprehensive comparison of multiparty secure additions with differential privacy
Slawomir Goryczka and Li Xiong · 2015
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally · 2015
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Differentially Private Distributed Optimization
Zhenqi Huang, Sayan Mitra, and Nitin Vaidya · 2015
Earlier work this paper cites.
Privacy for the protected (only)
Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, and Grigory Yaroslavtsev · 2015
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Using machine teaching to identify optimal training-set attacks on machine learners
Shike Mei and Xiaojin Zhu · 2015
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Scalable Bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams · 2015
Earlier work this paper cites.
Deep learning with elastic averaging SGD
Sixin Zhang, Anna E Choromanska, and Yann LeCun · 2015
Cited alongside, same era.
Machine teaching: An inverse problem to machine learning and an approach toward optimal education
Xiaojin Zhu · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Over-the-air function computation in sensor networks
Omid Abari, Hariharan Rahul, and Dina Katabi · 2016
Cited alongside, same era.
XPIR: Private information retrieval for everyone
Carlos Aguilar-Melchor, Joris Barrier, Laurent Fousse, and Marc-Olivier Killijian · 2016
Cited alongside, same era.
High-throughput semi-honest secure three-party computation with an honest majority
Optimal schemes for discrete distribution estimation under locally differential privacy
Min Ye and Alexander Barg · 2018
Later among the works it cites.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Later among the works it cites.
An economic analysis of privacy protection and statistical accuracy as social choices
John M Abowd and Ian M Schmutte · 2019
Closest in time.
QUOTIENT: two-party secure neural network training and prediction
Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, and Adrià Gascón · 2019
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Federated learning for medical imaging, 2019
ai.intel · 2019
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Communication-computation trade-offs in PIR
Asra Ali, Tancrède Lepoint, Sarvar Patel, Mariana Raykova, Phillipp Schoppmann, Karn Seth, and Kevin Yeo · 2019
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Toshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof, and Kazuma Ohara · 2016
Cited alongside, same era.
Practical secure aggregation for federated learning on user-held data
K. A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2016
Cited alongside, same era.
Communication lower bounds for statistical estimation problems via a distributed data processing inequality
Mark Braverman, Ankit Garg, Tengyu Ma, Huy L. Nguyen, and David P. Woodruff · 2016
Cited alongside, same era.
Gossip dual averaging for decentralized optimization of pairwise functions
Igor Colin, Aurélien Bellet, Joseph Salmon, and Stéphan Clémençon · 2016
Cited alongside, same era.
Intel SGX explained
Victor Costan and Srinivas Devadas · 2016
Cited alongside, same era.
CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, and John Wernsing · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Cited alongside, same era.
Closest in time.
Bounding user contributions: A bias-variance trade-off in differential privacy
Kareem Amin, Alex Kulesza, Andres Munoz, and Sergei Vassilvtiskii · 2019
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Android Trusty TEE
androidtrusty · 2019
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Private Federated Learning (NeurIPS 2019 Expo Talk Abstract)
Apple · 2019
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Arm TrustZone Technology
armtrustzone · 2019
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Stochastic gradient push for distributed deep learning
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, and Michael Rabbat · 2019
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The privacy blanket of the shuffle model
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2019
Closest in time.
Secure evaluation of quantized neural networks
Assi Barak, Daniel Escudero, Anders P. K. Dalskov, and Marcel Keller · 2019
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Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
Closest in time.
A little is enough: Circumventing defenses for distributed learning
Moran Baruch, Gilad Baruch, and Yoav Goldberg · 2019
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Scalable zero knowledge with no trusted setup
Eli Ben-Sasson, Iddo Bentov, Yinon Horesh, and Michael Riabzev · 2019
Closest in time.
Learning adversarially fair and transferable representations
Martín Bertrán, Natalia Martínez, Afroditi Papadaki, Qiang Qiu, Miguel R. D. Rodrigues, Galen Reeves, and Guillermo Sapiro · 2019
Closest in time.
Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
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Federated learning with autotuned communication-efficient secure aggregation
K. A. Bonawitz, Fariborz Salehi, Jakub Konečný, Brendan McMahan, and Marco Gruteser · 2019
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Zero-knowledge proofs on secret-shared data via fully linear PCPs
Dan Boneh, Elette Boyle, Henry Corrigan-Gibbs, Niv Gilboa, and Yuval Ishai · 2019
Closest in time.
The structure of optimal private tests for simple hypotheses
Clément L Canonne, Gautam Kamath, Audra McMillan, Adam Smith, and Jonathan Ullman · 2019
Closest in time.
Personalization of end-to-end speech recognition on mobile devices for named entities
Khe Chai Sim, Françoise Beaufays, Arnaud Benard, Dhruv Guliani, Andreas Kabel, Nikhil Khare, Tamar Lucassen, Petr Zadrazil, Harry Zhang, Leif Johnson, et al · 2019
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On the upload versus download cost for secure and private matrix multiplication
Wei-Ting Chang and Ravi Tandon · 2019
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Ekiden: A platform for confidentiality-preserving, trustworthy, and performant smart contracts
Raymond Cheng, Fan Zhang, Jernej Kos, Warren He, Nicholas Hynes, Noah Johnson, Ari Juels, Andrew Miller, and Dawn Song · 2019
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Distributed differential privacy via shuffling
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
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Distributed fixed point methods with compressed iterates
Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, and Martin Takáč · 2019
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The clara training framework authors, 2019
NVIDIA Clara · 2019
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Private information retrieval with sublinear online time
Henry Corrigan-Gibbs and Dmitry Kogan · 2019
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Deep learning-based classification of mesothelioma improves prediction of patient outcome
Pierre Courtiol, Charles Maussion, Matahi Moarii, Elodie Pronier, Samuel Pilcer, Meriem Sefta, Pierre Manceron, Sylvain Toldo, Mikhail Zaslavskiy, Nolwenn Le Stang, et al · 2019
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The federated future is ready for shipping
Walter de Brouwer · 2019
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Sever: A robust meta-algorithm for stochastic optimization
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart · 2019
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Theoretical guarantees for model auditing with finite adversaries
Mario Diaz, Peter Kairouz, Jiachun Liao, and Lalitha Sankar · 2019
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Layers of bias: A unified approach for understanding problems with risk assessment
Laurel Eckhouse, Kristian Lum, Cynthia Conti-Cook, and Julie Ciccolini · 2019
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GADMM: Fast and communication efficient framework for distributed machine learning
Anis Elgabli, Jihong Park, Amrit S Bedi, Mehdi Bennis, and Vaneet Aggarwal · 2019
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Amplification by shuffling: From local to central differential privacy via anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta · 2019
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Machine learning ledger orchestration for drug discovery, 2019
EU CORDIS · 2019
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Local model poisoning attacks to Byzantine-robust federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2019
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FeatureCloud: Our vision, 2019
FeatureCloud · 2019
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Weight agnostic neural networks
Adam Gaier and David Ha · 2019
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vqSGD: Vector quantized stochastic gradient descent
Venkata Gandikota, Raj Kumar Maity, and Arya Mazumdar · 2019
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Compressible FHE with applications to PIR
Craig Gentry and Shai Halevi · 2019
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Local SGD with periodic averaging: Tighter analysis and adaptive synchronization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, and Viveck R Cadambe · 2019
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Central server free federated learning over single-sided trust social networks
Chaoyang He, Conghui Tan, Hanlin Tang, Shuang Qiu, and Ji Liu · 2019
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Natural compression for distributed deep learning
Samuel Horvath, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, and Peter Richtarik · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Learning privately over distributed features: An admm sharing approach, 2019
Yaochen Hu, Peng Liu, Linglong Kong, and Di Niu · 2019
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On deploying secure computing commercially: Private intersection-sum protocols and their business applications
Mihaela Ion, Ben Kreuter, Ahmet Erhan Nergiz, Sarvar Patel, Mariana Raykova, Shobhit Saxena, Karn Seth, David Shanahan, and Moti Yung · 2019
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On the capacity of secure distributed matrix multiplication
Zhuqing Jia and Syed Ali Jafar · 2019
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Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečný, Keith Rush, and Sreeram Kannan · 2019
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Incentive design for efficient federated learning in mobile networks: A contract theory approach
Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, and Dong In Kim · 2019
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Error feedback fixes SignSGD and other gradient compression schemes
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian Stich, and Martin Jaggi · 2019
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Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar · 2019
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Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
Anastasia Koloskova, Sebastian U Stich, and Martin Jaggi · 2019
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Certified robustness to adversarial examples with differential privacy
Mathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
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libsnark: a c++ library for zkSNARK proofs
libsnark · 2019
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Minimax rates of estimating approximate differential privacy
Xiyang Liu and Sewoong Oh · 2019
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Real-world image datasets for federated learning
Jiahuan Luo, Xueyang Wu, Yun Luo, Anbu Huang, Yunfeng Huang, Yang Liu, and Qiang Yang · 2019
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Current clinical use of polygenic scores will risk exacerbating health disparities
Alicia R Martin, Masahiro Kanai, Yoichiro Kamatani, Yukinori Okada, Benjamin M Neale, and Mark J Daly · 2019
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Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Musketeer: About, 2019
Musketeer · 2019
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ON-OFF privacy with correlated requests
Carolina Naim, Fangwei Ye, and Salim El Rouayheb · 2019
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Secure federated submodel learning
Chaoyue Niu, Fan Wu, Shaojie Tang, Lifeng Hua, Rongfei Jia, Chengfei Lv, Zhihua Wu, and Guihai Chen · 2019
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Model compression by entropy penalized reparameterization
Deniz Oktay, Johannes Ballé, Saurabh Singh, and Abhinav Shrivastava · 2019
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PALISADE lattice cryptography library
Palisade · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Communication trade-offs for synchronized distributed SGD with large step size
Kumar Kshitij Patel and Aymeric Dieuleveut · 2019
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Google’s Sundar Pichai: Privacy Should Not Be a Luxury Good
Sundar Pichai · 2019
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AdaCliP: Adaptive clipping for private SGD
Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X Yu, Sashank J Reddi, and Sanjiv Kumar · 2019
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Robust aggregation for federated learning
Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui · 2019
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DETOX: A redundancy-based framework for faster and more robust gradient aggregation
Shashank Rajput, Hongyi Wang, Zachary Charles, and Dimitris Papailiopoulos · 2019
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Federated learning for emoji prediction in a mobile keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays · 2019
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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HEAX: High-performance architecture for computation on homomorphically encrypted data in the cloud
M Sadegh Riazi, Kim Laine, Blake Pelton, and Wei Dai · 2019
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Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice
Rashida Richardson, Jason Schultz, and Kate Crawford · 2019
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Honeycrisp: large-scale differentially private aggregation without a trusted core
Edo Roth, Daniel Noble, Brett Hemenway Falk, and Andreas Haeberlen · 2019
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Robust and communication-efficient federated learning from non-IID data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
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Adversarial training for free
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
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ExpertMatcher: Automating ML model selection for clients using hidden representations
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, and Ramesh Raskar · 2019
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Learning with bad training data via iterative trimmed loss minimization
Yanyao Shen and Sujay Sanghavi · 2019
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A comprehensive guide to Bayesian convolutional neural network with variational inference
Kumar Shridhar, Felix Laumann, and Marcus Liwicki · 2019
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Detailed comparison of communication efficiency of split learning and federated learning
Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, and Ramesh Raskar · 2019
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Privacy risks of securing machine learning models against adversarial examples
Liwei Song, Reza Shokri, and Prateek Mittal · 2019
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Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?
Brij Mohan Lal Srivastava, Aurélien Bellet, Marc Tommasi, and Emmanuel Vincent · 2019
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Local SGD converges fast and communicates little
Sebastian U Stich · 2019
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Sebastian U Stich and Sai Praneeth Karimireddy · 2019
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Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
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Your chats stay private while Messages improves suggestions, 2019
support.google · 2019
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DeepSqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression
Hanlin Tang, Xiangru Lian, Shuang Qiu, Lei Yuan, Ce Zhang, Tong Zhang, and Ji Liu · 2019
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Differentially private learning with adaptive clipping
Om Thakkar, Galen Andrew, and H Brendan McMahan · 2019
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Slalom: Fast, verifiable and private execution of neural networks in trusted hardware
Florian Tramèr and Dan Boneh · 2019
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Adversarial training and robustness for multiple perturbations
Florian Tramèr and Dan Boneh · 2019
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Google landmark dataset v2, 2019
The Google-Landmark v2 Authors · 2019
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PowerSGD: Practical low-rank gradient compression for distributed optimization
Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
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Jianyu Wang and Gauri Joshi · 2019
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WeBank and Swiss re signed cooperation MOU, 2019
WeBank · 2019
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Wasserstein adversarial examples via projected sinkhorn iterations
Eric Wong, Frank R Schmidt, and J Zico Kolter · 2019
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Zeno++: robust asynchronous SGD with arbitrary number of Byzantine workers
Cong Xie · 2019
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Preserving ON-OFF privacy for past and future requests
Fangwei Ye, Carolina Naim, and Salim El Rouayheb · 2019
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett · 2019
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Hao Yu, Rong Jin, and Sen Yang · 2019
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Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
Valentina Zantedeschi, Aurélien Bellet, and Marc Tommasi · 2019
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Yawei Zhao, Chen Yu, Peilin Zhao, and Ji Liu · 2019
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Federated heavy hitters discovery with differential privacy
Wennan Zhu, Peter Kairouz, Haicheng Sun, Brendan McMahan, and Wei Li · 2019
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Inference under information constraints i: Lower bounds from chi-square contraction
Jayadev Acharya, Clément L Canonne, and Himanshu Tyagi · 2020
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The power of the hybrid model for mean estimation
Brendan Avent, Yatharth Dubey, and Aleksandra Korolova · 2020
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Beyond individual and group fairness
Pranjal Awasthi, Corinna Cortes, Yishay Mansour, and Mehryar Mohri · 2020
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Private summation in the multi-message shuffle model
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2020
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Qsparse-local-sgd: Distributed sgd with quantization, sparsification, and local computations
Debraj Basu, Deepesh Data, Can Karakus, and Suhas N Diggavi · 2020
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Secure single-server aggregation with (poly)logarithmic overhead
James Henry Bell, Kallista A. Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova · 2020
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Flower: A friendly federated learning research framework, 2020
Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D. Lane · 2020
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What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2020
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On the outsized importance of learning rates in local update methods
Zachary Charles and Jakub Konečnỳ · 2020
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Multiple-source adaptation with domain classifiers
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, and Ningshan Zhang · 2020
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Privacy amplification by decentralization
Edwige Cyffers and Aurélien Bellet · 2020
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Data encoding for byzantine-resilient distributed optimization
Deepesh Data, Linqi Song, and Suhas Diggavi · 2020
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Personalized Federated Learning with Moreau Envelopes
Canh T. Dinh, Nguyen H. Tran, and Tuan Dung Nguyen · 2020
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Harnessing wireless channels for scalable and privacy-preserving federated learning, 2020
Anis Elgabli, Jihong Park, Chaouki Ben Issaid, and Mehdi Bennis · 2020
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Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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Shuffled model of federated learning: Privacy, communication and accuracy trade-offs
Antonious M Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz, and Ananda Theertha Suresh · 2020
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Fedboost: A communication-efficient algorithm for federated learning
Jenny Hamer, Mehryar Mohri, and Ananda Theertha Suresh · 2020
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Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
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Censored and fair universal representations using generative adversarial models
Peter Kairouz, Jiachun Liao, Chong Huang, and Lalitha Sankar · 2020
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Device Heterogeneity in Federated Learning: A Superquantile Approach
Yassine Laguel, Krishna Pillutla, Jérôme Malick, and Zaid Harchaoui · 2020
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Private join and compute from PIR with default
Tancrède Lepoint, Sarvar Patel, Mariana Raykova, Karn Seth, and Ni Trieu · 2020
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Don’t use large mini-batches, use local SGD
Tao Lin, Sebastian U Stich, and Martin Jaggi · 2020
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A secure federated transfer learning framework
Yang Liu, Yan Kang, Chaoping Xing, Tianjian Chen, and Qiang Yang · 2020
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IBM federated learning: An enterprise framework white paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, et al · 2020
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Towards fair and privacy-preserving federated deep models
Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu, and Kee Siong Ng · 2020
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Privacy in deep learning: A survey
Fatemehsadat Mireshghallah, Mohammadkazem Taram, , Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, and Esmaeilzadeh Hadi · 2020
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Decentralized gradient methods: does topology matter?
Giovanni Neglia, Chuan Xu, Don Towsley, and Gianmarco Calbi · 2020
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Tempered sigmoid activations for deep learning with differential privacy
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, and Úlfar Erlingsson · 2020
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Federated analytics: Collaborative data science without data collection, May 2020
Daniel Ramage and Stefano Mazzocchi · 2020
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Training production language models without memorizing user data
Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H Brendan McMahan, and Françoise Beaufays · 2020
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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Federated learning and differential privacy: Software tools analysis, the sherpa. ai fl framework and methodological guidelines for preserving data privacy
Nuria Rodríguez-Barroso, Goran Stipcich, Daniel Jiménez-López, José Antonio Ruiz-Millán, Eugenio Martínez-Cámara, Gerardo González-Seco, M Victoria Luzón, Miguel Angel Veganzones, and Francisco Herrera · 2020
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César Sabater, Aurélien Bellet, and Jan Ramon · 2020
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Microsoft SEAL (release 3.6)
SEAL · 2020
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https://github.com/google/shell-encryption , December 2020
SHELL · 2020
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DISCO: Dynamic and invariant sensitive channel obfuscation for deep neural networks
Abhishek Singh, Ayush Chopra, Vivek Sharma, Ethan Garza, Emily Zhang, Praneeth Vepakomma, and Ramesh Raskar · 2020
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Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh · 2020
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Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations
Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, and Jörn-Henrik Jacobsen · 2020
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Nopeek: Information leakage reduction to share activations in distributed deep learning
Praneeth Vepakomma, Otkrist Singh, Abhishek Gupta, and Ramesh Raskar · 2020
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Is local sgd better than minibatch sgd?
Blake Woodworth, Kumar Kshitij Patel, Sebastian U Stich, Zhen Dai, Brian Bullins, H Brendan McMahan, Ohad Shamir, and Nathan Srebro · 2020
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On distributed differential privacy and counting distinct elements
Lijie Chen, Badih Ghazi, Ravi Kumar, and Pasin Manurangsi · 2021
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