Preserving Privacy and Security in Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Nguyen, Truc, Thai, My T. |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Analysis of Privacy Leakage in Federated Large Language Models
by: Vu, Minh N., et al.
Published: (2024)
by: Vu, Minh N., et al.
Published: (2024)
Denial-of-Service Vulnerability of Hash-based Transaction Sharding: Attack and Countermeasure
by: Nguyen, Truc, et al.
Published: (2020)
by: Nguyen, Truc, et al.
Published: (2020)
Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning
by: Nguyen, Quan Minh, et al.
Published: (2026)
by: Nguyen, Quan Minh, et al.
Published: (2026)
Secure and Privacy-Preserving Federated Learning for Next-Generation Underground Mine Safety
by: Elmahallawy, Mohamed, et al.
Published: (2025)
by: Elmahallawy, Mohamed, et al.
Published: (2025)
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
by: Jeter, Tre' R., et al.
Published: (2023)
by: Jeter, Tre' R., et al.
Published: (2023)
DDP-SA: Scalable Privacy-Preserving Federated Learning via Distributed Differential Privacy and Secure Aggregation
by: Wei, Wenjing, et al.
Published: (2026)
by: Wei, Wenjing, et al.
Published: (2026)
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning
by: Yang, Xue, et al.
Published: (2024)
by: Yang, Xue, et al.
Published: (2024)
A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises
by: Fotohi, Reza, et al.
Published: (2025)
by: Fotohi, Reza, et al.
Published: (2025)
Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models
by: Nguyen, Quan, et al.
Published: (2025)
by: Nguyen, Quan, et al.
Published: (2025)
Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture
by: Fares, Mohamad Haj, et al.
Published: (2024)
by: Fares, Mohamad Haj, et al.
Published: (2024)
A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption
by: Nguyen, Khoa, et al.
Published: (2025)
by: Nguyen, Khoa, et al.
Published: (2025)
Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
by: Ngo, Hoang M., et al.
Published: (2026)
by: Ngo, Hoang M., et al.
Published: (2026)
Social-Aware Clustered Federated Learning with Customized Privacy Preservation
by: Wang, Yuntao, et al.
Published: (2022)
by: Wang, Yuntao, et al.
Published: (2022)
Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning
by: Hayati, Haleh, et al.
Published: (2024)
by: Hayati, Haleh, et al.
Published: (2024)
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
by: Li, Xiang, et al.
Published: (2025)
by: Li, Xiang, et al.
Published: (2025)
Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning
by: Damie, Marc, et al.
Published: (2025)
by: Damie, Marc, et al.
Published: (2025)
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
by: Xie, Chulin, et al.
Published: (2022)
by: Xie, Chulin, et al.
Published: (2022)
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection
by: Abadi, Aydin, et al.
Published: (2024)
by: Abadi, Aydin, et al.
Published: (2024)
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems
by: Aminifar, Amin, et al.
Published: (2024)
by: Aminifar, Amin, et al.
Published: (2024)
SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHE
by: Wang, Hongxiao, et al.
Published: (2022)
by: Wang, Hongxiao, et al.
Published: (2022)
BlocksecRT-DETR: Decentralized Privacy-Preserving and Token-Efficient Federated Transformer Learning for Secure Real-Time Object Detection in ITS
by: Tahera, Mohoshin Ara, et al.
Published: (2026)
by: Tahera, Mohoshin Ara, et al.
Published: (2026)
Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction
by: Zhong, Shuai, et al.
Published: (2024)
by: Zhong, Shuai, et al.
Published: (2024)
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks
by: Khoa, Tran Viet, et al.
Published: (2024)
by: Khoa, Tran Viet, et al.
Published: (2024)
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
by: Jiang, Yu, et al.
Published: (2024)
by: Jiang, Yu, et al.
Published: (2024)
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security
by: Ceviz, Ozlem, et al.
Published: (2023)
by: Ceviz, Ozlem, et al.
Published: (2023)
Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare
by: Nawaz, Anum, et al.
Published: (2025)
by: Nawaz, Anum, et al.
Published: (2025)
An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models
by: Ahmadi, Kasra, et al.
Published: (2025)
by: Ahmadi, Kasra, et al.
Published: (2025)
Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
by: Xu, Jie, et al.
Published: (2026)
by: Xu, Jie, et al.
Published: (2026)
Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
by: Saleem, Hamza, et al.
Published: (2024)
by: Saleem, Hamza, et al.
Published: (2024)
Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting
by: Ali, Mahad, et al.
Published: (2025)
by: Ali, Mahad, et al.
Published: (2025)
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
by: Jin, Weizhao, et al.
Published: (2023)
by: Jin, Weizhao, et al.
Published: (2023)
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)
Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration
by: Zhang, Yiwei, et al.
Published: (2025)
by: Zhang, Yiwei, et al.
Published: (2025)
Agentic Privacy-Preserving Machine Learning
by: Zhang, Mengyu, et al.
Published: (2025)
by: Zhang, Mengyu, et al.
Published: (2025)
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)
P3LS: Partial Least Squares under Privacy Preservation
by: Duy, Du Nguyen, et al.
Published: (2024)
by: Duy, Du Nguyen, et al.
Published: (2024)
GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption
by: Frimpong, Eugene, et al.
Published: (2024)
by: Frimpong, Eugene, 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)
FairDP: Certified Fairness with Differential Privacy
by: Tran, Khang, et al.
Published: (2023)
by: Tran, Khang, et al.
Published: (2023)
Similar Items
-
Analysis of Privacy Leakage in Federated Large Language Models
by: Vu, Minh N., et al.
Published: (2024) -
Denial-of-Service Vulnerability of Hash-based Transaction Sharding: Attack and Countermeasure
by: Nguyen, Truc, et al.
Published: (2020) -
Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning
by: Nguyen, Quan Minh, et al.
Published: (2026) -
Secure and Privacy-Preserving Federated Learning for Next-Generation Underground Mine Safety
by: Elmahallawy, Mohamed, et al.
Published: (2025) -
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
by: Jeter, Tre' R., et al.
Published: (2023)