Who Cited It

Federated Learning With Differential Privacy: Algorithms and Performance Analysis

2020 · IEEE Transactions on Information Forensics and Security · 2,357 citations · 1 from inside this corpus

Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farhad Farokhi, Shi Jin, Tony Q. S. Quek, H. Vincent Poor

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.

Federated Learning With Differential Privacy: Algorithms and Performance Analysis (2020)Federated Learning With Diffe…Federated Machine Learning (2019)Federated Machine LearningDeep Learning with Differential Privacy (2016)Deep Learning with Differenti…Communication-Efficient Learning of Deep Networks from Decentralized\n Data (2016)Communication-Efficient Learn…Communication-Efficient Learning of Deep Networks from Decentralized Data (2016)Communication-Efficient Learn…The Algorithmic Foundations of Differential Privacy (2014)The Algorithmic Foundations o…The Algorithmic Foundations of Differential Privacy (2013)The Algorithmic Foundations o…Federated Learning: Strategies for Improving Communication Efficiency (2016)Federated Learning: Strategie…Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures (2015)Model Inversion Attacks that …Privacy-Preserving Deep Learning (2015)Privacy-Preserving Deep Learn…Adaptive Federated Learning in Resource Constrained Edge Computing Systems (2019)Adaptive Federated Learning i…RAPPOR (2014)RAPPORA survey on security and privacy of federated learning (2020)A survey on security and priv…
12 of 12 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
Federated Machine Learning20196,228
Deep Learning with Differential Privacy20166,220
Communication-Efficient Learning of Deep Networks from Decentralized\n Data20165,645
Communication-Efficient Learning of Deep Networks from Decentralized Data20165,190
The Algorithmic Foundations of Differential Privacy20144,578
The Algorithmic Foundations of Differential Privacy20134,058
Federated Learning: Strategies for Improving Communication Efficiency20163,050
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures20152,885
Privacy-Preserving Deep Learning20152,325
Adaptive Federated Learning in Resource Constrained Edge Computing Systems20192,293
RAPPOR20141,548

What cites it, inside the corpus

PaperYearCited
A survey on security and privacy of federated learning20201,393

Links

DOI · Open access copy · OpenAlex record

Topics

Privacy-Preserving Technologies in DataComputer Science
Cryptography and Data SecurityComputer Science
Stochastic Gradient Optimization TechniquesComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports9 author record(s) attached.
  • supports58 reference(s) recorded.
  • supportsThe DOI's year agrees with the publication year.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:49+00:00.

sha256 a2172c1bbe00c44b…