Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
Fuente:
arXiv
Saved in:
| Main Authors: | Xu, Jie, Mehmood, Haaris, Van Dalen, Rogier, Saravanan, Karthikeyan, Ozay, Mete |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
by: Mehmood, Haaris, et al.
Published: (2026)
by: Mehmood, Haaris, et al.
Published: (2026)
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation
by: Xu, Jie, et al.
Published: (2024)
by: Xu, Jie, et al.
Published: (2024)
DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
by: Mehmood, Haaris, et al.
Published: (2026)
by: Mehmood, Haaris, et al.
Published: (2026)
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)
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)
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
by: Tang, Xinyu, et al.
Published: (2023)
by: Tang, Xinyu, et al.
Published: (2023)
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)
TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)
by: Karthikeyan, Harish, et al.
Published: (2026)
by: Karthikeyan, Harish, 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)
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
by: Heikkilä, Mikko A.
Published: (2024)
by: Heikkilä, Mikko A.
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)
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
by: Jiang, Yu, et al.
Published: (2024)
by: Jiang, Yu, et al.
Published: (2024)
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2024)
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2024)
Differentially Private Clustered Federated Learning
by: Malekmohammadi, Saber, et al.
Published: (2024)
by: Malekmohammadi, Saber, et al.
Published: (2024)
Differentially Private Federated Learning: A Systematic Review
by: Fu, Jie, et al.
Published: (2024)
by: Fu, Jie, et al.
Published: (2024)
Preserving Privacy and Security in Federated Learning
by: Nguyen, Truc, et al.
Published: (2022)
by: Nguyen, Truc, et al.
Published: (2022)
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
by: Ling, Xinpeng, et al.
Published: (2023)
by: Ling, Xinpeng, et al.
Published: (2023)
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)
Differentially Private Federated $k$-Means Clustering with Server-Side Data
by: Scott, Jonathan, et al.
Published: (2025)
by: Scott, Jonathan, et al.
Published: (2025)
Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)
by: Yang, Hillary, et al.
Published: (2024)
Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
by: Zhang, Meifan, et al.
Published: (2024)
by: Zhang, Meifan, et al.
Published: (2024)
Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy
by: Ganadily, Naif A., et al.
Published: (2024)
by: Ganadily, Naif A., et al.
Published: (2024)
Differentially Private Relational Learning with Entity-level Privacy Guarantees
by: Huang, Yinan, et al.
Published: (2025)
by: Huang, Yinan, et al.
Published: (2025)
Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024)
by: Gu, Yuechun, et al.
Published: (2024)
Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning
by: Behnia, Rouzbeh, et al.
Published: (2023)
by: Behnia, Rouzbeh, et al.
Published: (2023)
Privacy Preserving Data Imputation via Multi-party Computation for Medical Applications
by: Jentsch, Julia, et al.
Published: (2024)
by: Jentsch, Julia, 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)
Differentially Private Federated Learning With Time-Adaptive Privacy Spending
by: Kiani, Shahrzad, et al.
Published: (2025)
by: Kiani, Shahrzad, 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)
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
by: Schwethelm, Kristian, et al.
Published: (2024)
by: Schwethelm, Kristian, et al.
Published: (2024)
Clustering and Median Aggregation Improve Differentially Private Inference
by: Amin, Kareem, et al.
Published: (2025)
by: Amin, Kareem, et al.
Published: (2025)
The Normal Distributions Indistinguishability Spectrum and its Application to Privacy-Preserving Machine Learning
by: Wei, Yu, et al.
Published: (2023)
by: Wei, Yu, et al.
Published: (2023)
Samplable Anonymous Aggregation for Private Federated Data Analysis
by: Talwar, Kunal, et al.
Published: (2023)
by: Talwar, Kunal, et al.
Published: (2023)
Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation
by: Yang, Kun, et al.
Published: (2025)
by: Yang, Kun, et al.
Published: (2025)
Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning
by: Hayati, Haleh, et al.
Published: (2024)
by: Hayati, Haleh, et al.
Published: (2024)
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)
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
by: Xu, Runhua, et al.
Published: (2025)
by: Xu, Runhua, et al.
Published: (2025)
Bridging Local and Federated Data Normalization in Federated Learning: A Privacy-Preserving Approach
by: Coşğun, Melih, et al.
Published: (2025)
by: Coşğun, Melih, 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 Leakage via Output Label Space and Differentially Private Continual Learning
by: Tobaben, Marlon, et al.
Published: (2024)
by: Tobaben, Marlon, et al.
Published: (2024)
Similar Items
-
DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
by: Mehmood, Haaris, et al.
Published: (2026) -
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation
by: Xu, Jie, et al.
Published: (2024) -
DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
by: Mehmood, Haaris, et al.
Published: (2026) -
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
by: Piran, Fardin Jalil, 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)