Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhang, Xiaojin, Chen, Kai, Yang, Qiang |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning
por: Zhang, Xiaojin, et al.
Publicado: (2024)
por: Zhang, Xiaojin, et al.
Publicado: (2024)
Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
por: Zhang, Xiaojin, et al.
Publicado: (2025)
por: Zhang, Xiaojin, et al.
Publicado: (2025)
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory
por: Zhang, Xiaojin, et al.
Publicado: (2024)
por: Zhang, Xiaojin, et al.
Publicado: (2024)
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
por: Khalil, Mohammad, et al.
Publicado: (2025)
por: Khalil, Mohammad, et al.
Publicado: (2025)
Bridging Privacy and Robustness for Trustworthy Machine Learning
por: Zhang, Xiaojin, et al.
Publicado: (2024)
por: Zhang, Xiaojin, et al.
Publicado: (2024)
A Game-theoretic Framework for Privacy-preserving Federated Learning
por: Zhang, Xiaojin, et al.
Publicado: (2023)
por: Zhang, Xiaojin, et al.
Publicado: (2023)
Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
por: Xu, Yukai, et al.
Publicado: (2024)
por: Xu, Yukai, et al.
Publicado: (2024)
No Free Lunch Theorem for Privacy-Preserving LLM Inference
por: Zhang, Xiaojin, et al.
Publicado: (2024)
por: Zhang, Xiaojin, et al.
Publicado: (2024)
Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
por: Dong, Wenhan, et al.
Publicado: (2024)
por: Dong, Wenhan, et al.
Publicado: (2024)
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
por: Hiniduma, Kaveen, et al.
Publicado: (2025)
por: Hiniduma, Kaveen, et al.
Publicado: (2025)
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
por: Piran, Fardin Jalil, et al.
Publicado: (2024)
por: Piran, Fardin Jalil, et al.
Publicado: (2024)
Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
por: Piran, Fardin Jalil, et al.
Publicado: (2025)
por: Piran, Fardin Jalil, et al.
Publicado: (2025)
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
por: Li, Yuecheng, et al.
Publicado: (2024)
por: Li, Yuecheng, et al.
Publicado: (2024)
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
por: Scheliga, Daniel, et al.
Publicado: (2023)
por: Scheliga, Daniel, et al.
Publicado: (2023)
Privacy-Preserving in Blockchain-based Federated Learning Systems
por: M., Sameera K., et al.
Publicado: (2024)
por: M., Sameera K., et al.
Publicado: (2024)
Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
por: Seo, Jungwon, et al.
Publicado: (2026)
por: Seo, Jungwon, et al.
Publicado: (2026)
Data-Free Privacy-Preserving for LLMs via Model Inversion and Selective Unlearning
por: Zhou, Xinjie, et al.
Publicado: (2026)
por: Zhou, Xinjie, et al.
Publicado: (2026)
Decentralized Federated Learning: A Survey on Security and Privacy
por: Hallaji, Ehsan, et al.
Publicado: (2024)
por: Hallaji, Ehsan, et al.
Publicado: (2024)
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation
por: Ge, Shiming, et al.
Publicado: (2024)
por: Ge, Shiming, et al.
Publicado: (2024)
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
por: Rokvic, Ljubomir, et al.
Publicado: (2022)
por: Rokvic, Ljubomir, et al.
Publicado: (2022)
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
por: Yu, Sixing, et al.
Publicado: (2023)
por: Yu, Sixing, et al.
Publicado: (2023)
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
por: Corbucci, Luca, et al.
Publicado: (2024)
por: Corbucci, Luca, et al.
Publicado: (2024)
Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
por: Islam, MD Shafikul, et al.
Publicado: (2026)
por: Islam, MD Shafikul, et al.
Publicado: (2026)
FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
por: Gu, Hanlin, et al.
Publicado: (2024)
por: Gu, Hanlin, et al.
Publicado: (2024)
KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation
por: Kotal, Anantaa, et al.
Publicado: (2024)
por: Kotal, Anantaa, et al.
Publicado: (2024)
UIFV: Data Reconstruction Attack in Vertical Federated Learning
por: Yang, Jirui, et al.
Publicado: (2024)
por: Yang, Jirui, et al.
Publicado: (2024)
Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings
por: Arazzi, Marco, et al.
Publicado: (2025)
por: Arazzi, Marco, et al.
Publicado: (2025)
FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models
por: Ye, Pukang, et al.
Publicado: (2025)
por: Ye, Pukang, et al.
Publicado: (2025)
A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data
por: Negoya, Yusaku, et al.
Publicado: (2025)
por: Negoya, Yusaku, et al.
Publicado: (2025)
Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection
por: Lange, Lucas, et al.
Publicado: (2024)
por: Lange, Lucas, et al.
Publicado: (2024)
Near-Optimal Reinforcement Learning with Shuffle Differential Privacy
por: Bai, Shaojie, et al.
Publicado: (2024)
por: Bai, Shaojie, et al.
Publicado: (2024)
Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)
por: Abadi, Aydin, et al.
Publicado: (2024)
por: Abadi, Aydin, et al.
Publicado: (2024)
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
por: Liu, Zheyuan, et al.
Publicado: (2023)
por: Liu, Zheyuan, et al.
Publicado: (2023)
BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks
por: Yadav, Sushilkumar, et al.
Publicado: (2024)
por: Yadav, Sushilkumar, et al.
Publicado: (2024)
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
por: Xu, Runhua, et al.
Publicado: (2025)
por: Xu, Runhua, et al.
Publicado: (2025)
Towards Family-Grouped Hierarchical Federated Learning on Sub-5KB Models: A Feasibility Study of Privacy-Preserving ECG Monitoring for Ultra-Resource-Constrained Wearables
por: Wu, Hangyu
Publicado: (2026)
por: Wu, Hangyu
Publicado: (2026)
PassREfinder-FL: Privacy-Preserving Credential Stuffing Risk Prediction via Graph-Based Federated Learning for Representing Password Reuse between Websites
por: Kim, Jaehan, et al.
Publicado: (2025)
por: Kim, Jaehan, et al.
Publicado: (2025)
FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
por: Wang, Ruiyang, et al.
Publicado: (2026)
por: Wang, Ruiyang, et al.
Publicado: (2026)
Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense
por: Li, Wenjie, et al.
Publicado: (2024)
por: Li, Wenjie, et al.
Publicado: (2024)
Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
por: Díaz, Judith Sáinz-Pardo, et al.
Publicado: (2026)
por: Díaz, Judith Sáinz-Pardo, et al.
Publicado: (2026)
Ejemplares similares
-
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning
por: Zhang, Xiaojin, et al.
Publicado: (2024) -
Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
por: Zhang, Xiaojin, et al.
Publicado: (2025) -
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory
por: Zhang, Xiaojin, et al.
Publicado: (2024) -
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
por: Khalil, Mohammad, et al.
Publicado: (2025) -
Bridging Privacy and Robustness for Trustworthy Machine Learning
por: Zhang, Xiaojin, et al.
Publicado: (2024)