ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
Fuente:
arXiv
Saved in:
| Main Authors: | Kato, Fumiyuki, Xiong, Li, Takagi, Shun, Cao, Yang, Yoshikawa, Masatoshi |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization
by: Takagi, Shun, et al.
Published: (2024)
by: Takagi, Shun, et al.
Published: (2024)
Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
by: Zheng, Shuyuan, et al.
Published: (2022)
by: Zheng, Shuyuan, et al.
Published: (2022)
Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing
by: Takagi, Shun, et al.
Published: (2025)
by: Takagi, Shun, et al.
Published: (2025)
SoK: Verifiable Cross-Silo FL
by: Korneev, Aleksei, et al.
Published: (2024)
by: Korneev, Aleksei, et al.
Published: (2024)
Graph Learning Across Data Silos
by: Zhang, Xiang, et al.
Published: (2023)
by: Zhang, Xiang, et al.
Published: (2023)
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
by: Xu, Jiahao, et al.
Published: (2025)
by: Xu, Jiahao, et al.
Published: (2025)
Analysis of Shuffling Beyond Pure Local Differential Privacy
by: Takagi, Shun, et al.
Published: (2026)
by: Takagi, Shun, et al.
Published: (2026)
DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
by: Zhang, Qiuchen, et al.
Published: (2022)
by: Zhang, Qiuchen, et al.
Published: (2022)
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
by: Zheng, Shuyuan, et al.
Published: (2025)
by: Zheng, Shuyuan, et al.
Published: (2025)
Convergent Differential Privacy Analysis for General Federated Learning
by: Sun, Yan, et al.
Published: (2024)
by: Sun, Yan, et al.
Published: (2024)
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)
FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning
by: Yang, Yanxin, et al.
Published: (2023)
by: Yang, Yanxin, et al.
Published: (2023)
LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples
by: Wu, Huiyu, et al.
Published: (2024)
by: Wu, Huiyu, et al.
Published: (2024)
Federated Graph Analytics with Differential Privacy
by: Liu, Shang, et al.
Published: (2024)
by: Liu, Shang, 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)
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
by: You, Zhichao, et al.
Published: (2025)
by: You, Zhichao, et al.
Published: (2025)
FL-Defender: Combating Targeted Attacks in Federated Learning
by: Jebreel, Najeeb, et al.
Published: (2022)
by: Jebreel, Najeeb, et al.
Published: (2022)
Cross-silo Federated Learning with Record-level Personalized Differential Privacy
by: Liu, Junxu, et al.
Published: (2024)
by: Liu, Junxu, et al.
Published: (2024)
Federated Transfer Learning with Differential Privacy
by: Li, Mengchu, et al.
Published: (2024)
by: Li, Mengchu, et al.
Published: (2024)
FuSeFL: Fully Secure and Scalable Federated Learning
by: Ghinani, Sahar Ghoflsaz, et al.
Published: (2025)
by: Ghinani, Sahar Ghoflsaz, et al.
Published: (2025)
Extracting Spatiotemporal Data from Gradients with Large Language Models
by: Zheng, Lele, et al.
Published: (2024)
by: Zheng, Lele, et al.
Published: (2024)
Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy
by: Oliveira, Júlio, et al.
Published: (2026)
by: Oliveira, Júlio, et al.
Published: (2026)
Complex-valued Federated Learning with Differential Privacy and MRI Applications
by: Riess, Anneliese, et al.
Published: (2021)
by: Riess, Anneliese, et al.
Published: (2021)
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy
by: Bao, Ergute, et al.
Published: (2022)
by: Bao, Ergute, et al.
Published: (2022)
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning
by: Khan, Momin Ahmad, et al.
Published: (2024)
by: Khan, Momin Ahmad, et al.
Published: (2024)
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning
by: Chua, Lynn, et al.
Published: (2024)
by: Chua, Lynn, et al.
Published: (2024)
DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule
by: Matsumoto, Tomoya, et al.
Published: (2026)
by: Matsumoto, Tomoya, et al.
Published: (2026)
On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy
by: Wang, Zijian, et al.
Published: (2025)
by: Wang, Zijian, et al.
Published: (2025)
Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
by: Zheng, Lele, et al.
Published: (2024)
by: Zheng, Lele, et al.
Published: (2024)
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)
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
by: Feng, Shuya, et al.
Published: (2024)
by: Feng, Shuya, et al.
Published: (2024)
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates
by: Wang, Chuanyin, et al.
Published: (2025)
by: Wang, Chuanyin, et al.
Published: (2025)
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness
by: Salehi, Hojat Allah, et al.
Published: (2024)
by: Salehi, Hojat Allah, 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)
SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)
by: Liang, Shuang, et al.
Published: (2026)
by: Liang, Shuang, et al.
Published: (2026)
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)
Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
by: Gao, Fengyu, et al.
Published: (2024)
by: Gao, Fengyu, et al.
Published: (2024)
FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients
by: Zhang, Jianyi, et al.
Published: (2025)
by: Zhang, Jianyi, et al.
Published: (2025)
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning
by: Alsulaimawi, Zahir
Published: (2024)
by: Alsulaimawi, Zahir
Published: (2024)
Wasserstein Differential Privacy
by: Yang, Chengyi, et al.
Published: (2024)
by: Yang, Chengyi, et al.
Published: (2024)
Similar Items
-
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization
by: Takagi, Shun, et al.
Published: (2024) -
Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
by: Zheng, Shuyuan, et al.
Published: (2022) -
Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing
by: Takagi, Shun, et al.
Published: (2025) -
SoK: Verifiable Cross-Silo FL
by: Korneev, Aleksei, et al.
Published: (2024) -
Graph Learning Across Data Silos
by: Zhang, Xiang, et al.
Published: (2023)