A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Xiaojin, Xu, Mingcong, Chen, Wei |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
by: Zhang, Xiaojin, et al.
Published: (2023)
by: Zhang, Xiaojin, et al.
Published: (2023)
Bridging Privacy and Robustness for Trustworthy Machine Learning
by: Zhang, Xiaojin, et al.
Published: (2024)
by: Zhang, Xiaojin, et al.
Published: (2024)
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory
by: Zhang, Xiaojin, et al.
Published: (2024)
by: Zhang, Xiaojin, et al.
Published: (2024)
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
by: Li, Yuecheng, et al.
Published: (2024)
by: Li, Yuecheng, et al.
Published: (2024)
Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation
by: Yin, Yi, et al.
Published: (2025)
by: Yin, Yi, et al.
Published: (2025)
Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
by: Zhang, Xiaojin, et al.
Published: (2025)
by: Zhang, Xiaojin, et al.
Published: (2025)
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
by: Kang, Yan, et al.
Published: (2022)
by: Kang, Yan, et al.
Published: (2022)
Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
by: Ahamed, Sayyed Farid, et al.
Published: (2024)
by: Ahamed, Sayyed Farid, et al.
Published: (2024)
A Game-theoretic Framework for Privacy-preserving Federated Learning
by: Zhang, Xiaojin, et al.
Published: (2023)
by: Zhang, Xiaojin, et al.
Published: (2023)
Optimizing Privacy-Utility Trade-off in Decentralized Learning with Generalized Correlated Noise
by: Rodio, Angelo, et al.
Published: (2025)
by: Rodio, Angelo, et al.
Published: (2025)
Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
by: Xu, Yukai, et al.
Published: (2024)
by: Xu, Yukai, et al.
Published: (2024)
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
by: Corbucci, Luca, et al.
Published: (2024)
by: Corbucci, Luca, et al.
Published: (2024)
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
by: Diana, Francesco, et al.
Published: (2025)
by: Diana, Francesco, et al.
Published: (2025)
Synthetic Data: Revisiting the Privacy-Utility Trade-off
by: Sarmin, Fatima Jahan, et al.
Published: (2024)
by: Sarmin, Fatima Jahan, et al.
Published: (2024)
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
by: Hiniduma, Kaveen, et al.
Published: (2025)
by: Hiniduma, Kaveen, et al.
Published: (2025)
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
by: Khalil, Mohammad, et al.
Published: (2025)
by: Khalil, Mohammad, et al.
Published: (2025)
Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
by: Dong, Wenhan, et al.
Published: (2024)
by: Dong, Wenhan, et al.
Published: (2024)
Decentralized Federated Learning: A Survey on Security and Privacy
by: Hallaji, Ehsan, et al.
Published: (2024)
by: Hallaji, Ehsan, et al.
Published: (2024)
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
by: Piran, Fardin Jalil, et al.
Published: (2024)
by: Piran, Fardin Jalil, et al.
Published: (2024)
Privacy-Preserving in Blockchain-based Federated Learning Systems
by: M., Sameera K., et al.
Published: (2024)
by: M., Sameera K., et al.
Published: (2024)
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
by: Scheliga, Daniel, et al.
Published: (2023)
by: Scheliga, Daniel, et al.
Published: (2023)
Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models
by: Zhang, Fuyao, et al.
Published: (2025)
by: Zhang, Fuyao, et al.
Published: (2025)
Data Valuation and Detections in Federated Learning
by: Li, Wenqian, et al.
Published: (2023)
by: Li, Wenqian, et al.
Published: (2023)
Federated Split Learning for Human Activity Recognition with Differential Privacy
by: Ndeko, Josue, et al.
Published: (2024)
by: Ndeko, Josue, et al.
Published: (2024)
Privacy Assessment of Federated Learning using Private Personalized Layers
by: Jourdan, Théo, et al.
Published: (2021)
by: Jourdan, Théo, et al.
Published: (2021)
Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
by: Lee, Joohyung, et al.
Published: (2023)
by: Lee, Joohyung, et al.
Published: (2023)
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
by: Rokvic, Ljubomir, et al.
Published: (2022)
by: Rokvic, Ljubomir, et al.
Published: (2022)
Generalist++: A Meta-learning Framework for Mitigating Trade-off in Adversarial Training
by: Wang, Yisen, et al.
Published: (2025)
by: Wang, Yisen, et al.
Published: (2025)
Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
by: Islam, MD Shafikul, et al.
Published: (2026)
by: Islam, MD Shafikul, et al.
Published: (2026)
Cross-silo Federated Learning with Record-level Personalized Differential Privacy
by: Liu, Junxu, et al.
Published: (2024)
by: Liu, Junxu, et al.
Published: (2024)
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning
by: Németh, Gergely Dániel, et al.
Published: (2023)
by: Németh, Gergely Dániel, et al.
Published: (2023)
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
by: Yu, Sixing, et al.
Published: (2023)
by: Yu, Sixing, et al.
Published: (2023)
ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation
by: Shao, Wei, et al.
Published: (2024)
by: Shao, Wei, et al.
Published: (2024)
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
by: Yu, Lei, et al.
Published: (2024)
by: Yu, Lei, et al.
Published: (2024)
Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via $f$-Differential Privacy
by: Li, Xiang, et al.
Published: (2025)
by: Li, Xiang, et al.
Published: (2025)
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
by: Liu, Zheyuan, et al.
Published: (2023)
by: Liu, Zheyuan, et al.
Published: (2023)
UIFV: Data Reconstruction Attack in Vertical Federated Learning
by: Yang, Jirui, et al.
Published: (2024)
by: Yang, Jirui, et al.
Published: (2024)
Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials
by: Tertulino, Rodrigo, et al.
Published: (2026)
by: Tertulino, Rodrigo, et al.
Published: (2026)
FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning
by: Zeng, Zhihao, et al.
Published: (2025)
by: Zeng, Zhihao, et al.
Published: (2025)
Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings
by: Arazzi, Marco, et al.
Published: (2025)
by: Arazzi, Marco, et al.
Published: (2025)
Similar Items
-
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
by: Zhang, Xiaojin, et al.
Published: (2023) -
Bridging Privacy and Robustness for Trustworthy Machine Learning
by: Zhang, Xiaojin, et al.
Published: (2024) -
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory
by: Zhang, Xiaojin, et al.
Published: (2024) -
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
by: Li, Yuecheng, et al.
Published: (2024) -
Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation
by: Yin, Yi, et al.
Published: (2025)