Complex-valued Federated Learning with Differential Privacy and MRI Applications
Fuente:
arXiv
Saved in:
| Main Authors: | Riess, Anneliese, Ziller, Alexander, Kolek, Stefan, Rueckert, Daniel, Schnabel, Julia, Kaissis, Georgios |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks
by: Riess, Anneliese, et al.
Published: (2024)
by: Riess, Anneliese, et al.
Published: (2024)
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy
by: Kaissis, Georgios, et al.
Published: (2024)
by: Kaissis, Georgios, et al.
Published: (2024)
Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy
by: Kaiser, Johannes, et al.
Published: (2026)
by: Kaiser, Johannes, et al.
Published: (2026)
Optimal conversion from Rényi Differential Privacy to $f$-Differential Privacy
by: Riess, Anneliese, et al.
Published: (2026)
by: Riess, Anneliese, et al.
Published: (2026)
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
by: Schwethelm, Kristian, et al.
Published: (2024)
by: Schwethelm, Kristian, et al.
Published: (2024)
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training
by: Usynin, Dmitrii, et al.
Published: (2023)
by: Usynin, Dmitrii, et al.
Published: (2023)
Visual Privacy Auditing with Diffusion Models
by: Schwethelm, Kristian, et al.
Published: (2024)
by: Schwethelm, Kristian, et al.
Published: (2024)
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
by: Gomez, Juan Felipe, et al.
Published: (2025)
by: Gomez, Juan Felipe, et al.
Published: (2025)
Attack-Aware Noise Calibration for Differential Privacy
by: Kulynych, Bogdan, et al.
Published: (2024)
by: Kulynych, Bogdan, et al.
Published: (2024)
Convergent Differential Privacy Analysis for General Federated Learning
by: Sun, Yan, et al.
Published: (2024)
by: Sun, Yan, et al.
Published: (2024)
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
by: Arasteh, Soroosh Tayebi, et al.
Published: (2023)
by: Arasteh, Soroosh Tayebi, et al.
Published: (2023)
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)
Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy
by: Oliveira, Júlio, et al.
Published: (2026)
by: Oliveira, Júlio, et al.
Published: (2026)
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)
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)
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)
Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy
by: Kulynych, Bogdan, et al.
Published: (2025)
by: Kulynych, Bogdan, et al.
Published: (2025)
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)
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
by: Kato, Fumiyuki, et al.
Published: (2023)
by: Kato, Fumiyuki, et al.
Published: (2023)
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)
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)
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)
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
by: Jiang, Yu, et al.
Published: (2024)
by: Jiang, Yu, et al.
Published: (2024)
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 Transfer Learning with Differential Privacy
by: Li, Mengchu, et al.
Published: (2024)
by: Li, Mengchu, 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)
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)
On the Efficiency of Privacy Attacks in Federated Learning
by: Tabassum, Nawrin, et al.
Published: (2024)
by: Tabassum, Nawrin, 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)
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)
The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2025)
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2025)
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)
Federated Split Learning for Human Activity Recognition with Differential Privacy
by: Ndeko, Josue, et al.
Published: (2024)
by: Ndeko, Josue, et al.
Published: (2024)
DPxFin: Adaptive Differential Privacy for Anti-Money Laundering Detection via Reputation-Weighted Federated Learning
by: Kanagavelu, Renuga, et al.
Published: (2026)
by: Kanagavelu, Renuga, et al.
Published: (2026)
Survey of Privacy Threats and Countermeasures in Federated Learning
by: Hayashitani, Masahiro, et al.
Published: (2024)
by: Hayashitani, Masahiro, et al.
Published: (2024)
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)
FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy
by: Diaa, Abdulrahman, et al.
Published: (2024)
by: Diaa, Abdulrahman, et al.
Published: (2024)
Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge
by: Bertoli, Gustavo de Carvalho
Published: (2026)
by: Bertoli, Gustavo de Carvalho
Published: (2026)
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
by: Zhao, Tianyu, et al.
Published: (2025)
by: Zhao, Tianyu, et al.
Published: (2025)
Cross-silo Federated Learning with Record-level Personalized Differential Privacy
by: Liu, Junxu, et al.
Published: (2024)
by: Liu, Junxu, et al.
Published: (2024)
Similar Items
-
From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks
by: Riess, Anneliese, et al.
Published: (2024) -
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy
by: Kaissis, Georgios, et al.
Published: (2024) -
Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy
by: Kaiser, Johannes, et al.
Published: (2026) -
Optimal conversion from Rényi Differential Privacy to $f$-Differential Privacy
by: Riess, Anneliese, et al.
Published: (2026) -
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
by: Schwethelm, Kristian, et al.
Published: (2024)