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 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.
this paper
works it cites
works citing it
node size = global citations · hover for the full title
Citation edge evidence
- A survey on security and privacy of federated learning→Federated Learning With Differential Privacy: Algorithms and Performance AnalysisLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→Federated Machine LearningLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→Communication-Efficient Learning of Deep Networks from Decentralized\n DataLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→Federated Learning: Strategies for Improving Communication EfficiencyLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→Adaptive Federated Learning in Resource Constrained Edge Computing SystemsLegacy edge — source detail unavailable
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis→RAPPORLegacy edge — source detail unavailable
- Privacy-Preserving Deep Learning→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
- Privacy-Preserving Deep Learning→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
- Deep Learning with Differential Privacy→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
- Deep Learning with Differential Privacy→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
- Deep Learning with Differential Privacy→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
- Deep Learning with Differential Privacy→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
- Federated Machine Learning→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
- Federated Machine Learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
- Adaptive Federated Learning in Resource Constrained Edge Computing Systems→Federated Learning: Strategies for Improving Communication EfficiencyLegacy edge — source detail unavailable
- Adaptive Federated Learning in Resource Constrained Edge Computing Systems→Communication-Efficient Learning of Deep Networks from Decentralized\n DataLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Federated Learning: Strategies for Improving Communication EfficiencyLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
- The Algorithmic Foundations of Differential Privacy→Communication-Efficient Learning of Deep Networks from Decentralized\n DataLegacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| A survey on security and privacy of federated learning | 2020 | 1,393 |
Links
Download BibTeX · Download RIS · DOI · Open access copy · OpenAlex record
Topics
| Privacy-Preserving Technologies in Data | Computer Science |
| Cryptography and Data Security | Computer Science |
| Stochastic Gradient Optimization Techniques | Computer 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
sha256 a2172c1bbe00c44b…