A survey on security and privacy of federated learning
Viraaji Mothukuri low, Reza M. Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, Gautam Srivastava
No abstract in the source record.
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→A Survey on Transfer LearningLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Advances in neural information processing systems 7Legacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Advances in Neural Information Processing Systems 29Legacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Differential PrivacyLegacy 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
- A survey on security and privacy of federated learning→Federated Learning: Challenges, Methods, and Future DirectionsLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Membership Inference Attacks Against Machine Learning ModelsLegacy 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→Federated Learning in Mobile Edge Networks: A Comprehensive SurveyLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Federated Learning With Differential Privacy: Algorithms and Performance AnalysisLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Ensemble Adversarial Training: Attacks and DefensesLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Privacy-Preserving Deep Learning via Additively Homomorphic EncryptionLegacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…Legacy edge — source detail unavailable
- A survey on security and privacy of federated learning→Client Selection for Federated Learning with Heterogeneous Resources in Mobile EdgeLegacy edge — source detail unavailable
- Robust De-anonymization of Large Sparse Datasets→Differential PrivacyLegacy edge — source detail unavailable
- Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
- Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Federated Learning: Strategies for Improving Communication EfficiencyLegacy edge — source detail unavailable
- Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey→A Survey on Transfer LearningLegacy edge — source detail unavailable
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Federated Learning: Strategies for Improving Communication EfficiencyLegacy edge — source detail unavailable
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Privacy-Preserving Deep Learning via Additively Homomorphic EncryptionLegacy 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→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
- Federated Learning: Challenges, Methods, and Future Directions→Federated Learning: Strategies for Improving Communication EfficiencyLegacy edge — source detail unavailable
- Federated Learning: Challenges, Methods, and Future Directions→Client Selection for Federated Learning with Heterogeneous Resources in Mobile EdgeLegacy edge — source detail unavailable
What this paper cites, inside the corpus
Links
Topics
| Privacy-Preserving Technologies in Data | Computer Science |
| Adversarial Robustness in Machine Learning | Computer Science |
| Cryptography and Data Security | Computer Science |
Is this record sound?
complete
Nothing in this record contradicts itself and no field we check is missing.
- supports6 author record(s) attached.
- supports296 reference(s) recorded.
- supportsThe DOI's year agrees with the publication year.
- supportsA title is present.
Provenance
sha256 59007f057c7dae86…