Who Cited It

Advances and Open Problems in Federated Learning

2020 · Foundations and Trends® in Machine Learning · 5,383 citations · 1 from inside this corpus

Peter Kairouz, H. Brendan McMahan, Brendan Avent low, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz low, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner low, David Evans, Joshua Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lingxiao He, Zhouyuan Huo low, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak low, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo low, Tancrède Lepoint, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song low, Sebastian U. Stich low, Ziteng Sun low, Florian Tramèr, Praneeth Vepakomma, Li Xiong, Zheng Xu, Sen Zhao

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Advances and Open Problems in Federated Learning (2020)Advances and Open Problems in…A Survey on Transfer Learning (2009)A Survey on Transfer LearningAdaptive Subgradient Methods for Online Learning and Stochastic Optimization (2010)Adaptive Subgradient Methods …Calibrating Noise to Sensitivity in Private Data Analysis (2006)Calibrating Noise to Sensitiv…Fully homomorphic encryption using ideal lattices (2009)Fully homomorphic encryption …Federated Machine Learning (2019)Federated Machine LearningDeep Learning with Differential Privacy (2016)Deep Learning with Differenti…The Byzantine Generals Problem (1982)The Byzantine Generals ProblemModel-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (2017)Model-Agnostic Meta-Learning …Intriguing properties of neural networks (2013)Intriguing properties of neur…Prototypical Networks for Few-shot Learning (2017)Prototypical Networks for Few…Communication-Efficient Learning of Deep Networks from Decentralized Data (2016)Communication-Efficient Learn…Membership Inference Attacks Against Machine Learning Models (2017)Membership Inference Attacks …The Sybil Attack (2002)The Sybil AttackUntraceable electronic mail, return addresses, and digital pseudonyms (1981)Untraceable electronic mail, …Tor: The Second-Generation Onion Router (2004)Tor: The Second-Generation On…The Algorithmic Foundations of Differential Privacy (2013)The Algorithmic Foundations o…Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017)Practical Secure Aggregation …A theory of learning from different domains (2009)A theory of learning from dif…Practical Black-Box Attacks against Machine Learning (2017)Practical Black-Box Attacks a…Fairness through awareness (2012)Fairness through awarenessDifferential Privacy: A Survey of Results (2008)Differential Privacy: A Surve…The Knowledge Complexity of Interactive Proof Systems (1989)The Knowledge Complexity of I…Algorithms for Hyper-Parameter Optimization (2011)Algorithms for Hyper-Paramete…Federated Learning: Strategies for Improving Communication Efficiency (2016)Federated Learning: Strategie…Privacy-preserving data mining (2000)Privacy-preserving data miningModel Inversion Attacks that Exploit Confidence Information and Basic Countermeasures (2015)Model Inversion Attacks that …Protocols for secure computations (1982)Optimization as a Model for Few-Shot Learning (2017)Optimization as a Model for F…SecureML: A System for Scalable Privacy-Preserving Machine Learning (2017)SecureML: A System for Scalab…Ensemble Adversarial Training: Attacks and Defenses (2017)Ensemble Adversarial Training…Our Data, Ourselves: Privacy Via Distributed Noise Generation (2006)Our Data, Ourselves: Privacy …(Leveled) fully homomorphic encryption without bootstrapping (2012)(Leveled) fully homomorphic e…Private information retrieval (1998)Private information retrievalTowards Deep Learning Models Resistant to Adversarial Attacks (2017)Towards Deep Learning Models …Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks (2019)Neural Cleanse: Identifying a…Text Data Augmentation for Deep Learning (2021)Text Data Augmentation for De…
36 of 36 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
A Survey on Transfer Learning200923,670
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization20108,621
Calibrating Noise to Sensitivity in Private Data Analysis20067,210
Fully homomorphic encryption using ideal lattices20096,701
Federated Machine Learning20196,228
Deep Learning with Differential Privacy20166,220
The Byzantine Generals Problem19826,037
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks20175,794
Intriguing properties of neural networks20135,739
Prototypical Networks for Few-shot Learning20175,204
Communication-Efficient Learning of Deep Networks from Decentralized Data20165,190
Membership Inference Attacks Against Machine Learning Models20174,490
The Sybil Attack20024,424
Untraceable electronic mail, return addresses, and digital pseudonyms19814,325
Tor: The Second-Generation Onion Router20044,079
The Algorithmic Foundations of Differential Privacy20134,058
Practical Secure Aggregation for Privacy-Preserving Machine Learning20173,744
A theory of learning from different domains20093,607
Practical Black-Box Attacks against Machine Learning20173,540
Fairness through awareness20123,476
Differential Privacy: A Survey of Results20083,392
The Knowledge Complexity of Interactive Proof Systems19893,291
Algorithms for Hyper-Parameter Optimization20113,188
Federated Learning: Strategies for Improving Communication Efficiency20163,050
Privacy-preserving data mining20002,997
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures20152,885
Protocols for secure computations19822,727
Optimization as a Model for Few-Shot Learning20172,436
SecureML: A System for Scalable Privacy-Preserving Machine Learning20171,899
Ensemble Adversarial Training: Attacks and Defenses20171,861
Our Data, Ourselves: Privacy Via Distributed Noise Generation20061,826
(Leveled) fully homomorphic encryption without bootstrapping20121,777
Private information retrieval19981,657
Towards Deep Learning Models Resistant to Adversarial Attacks20171,536
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks20191,404

What cites it, inside the corpus

PaperYearCited
Text Data Augmentation for Deep Learning20211,701

Links

DOI · Open access copy · OpenAlex record

Topics

Privacy-Preserving Technologies in DataComputer Science
Cryptography and Data SecurityComputer Science
Mobile Crowdsensing and CrowdsourcingComputer Science

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complete

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  • supports51 author record(s) attached.
  • supports451 reference(s) recorded.
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  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:44+00:00.

sha256 648f8491aaceaa34…