Cover Image

Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Díaz, Judith Sáinz-Pardo, Athanasiou, Andreas, Jung, Kangsoo, Palamidessi, Catuscia, García, Álvaro López
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Protection against Source Inference Attacks in Federated Learning
by: Athanasiou, Andreas, et al.
Published: (2026)

Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling
by: Athanasiou, Andreas, et al.
Published: (2024)

Mitigating Membership Inference Vulnerability in Personalized Federated Learning
by: Jung, Kangsoo, et al.
Published: (2025)

Tight Differential Privacy Guarantees for the Shuffle Model with $k$-Randomized Response
by: Biswas, Sayan, et al.
Published: (2022)

Self-Defense: Optimal QIF Solutions and Application to Website Fingerprinting
by: Athanasiou, Andreas, et al.
Published: (2024)

Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2026)

Causal Discovery Under Local Privacy
by: Binkytė, Rūta, et al.
Published: (2023)

Bayes Security: A Not So Average Metric
by: Chatzikokolakis, Konstantinos, et al.
Published: (2020)

Jeffrey's update rule as a minimizer of Kullback-Leibler divergence
by: Pinzón, Carlos, et al.
Published: (2025)

On the Consistency and Performance of the Iterative Bayesian Update
by: ElSalamouny, Ehab, et al.
Published: (2025)

A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
by: Makhlouf, Karima, et al.
Published: (2024)

Information Leakage Envelopes
by: Saeidian, Sara, et al.
Published: (2026)

Beyond Epsilon: A Principled QIF Framework for Local Differential Privacy
by: Gonze, Ramon G., et al.
Published: (2026)

An Open Source Python Library for Anonymizing Sensitive Data
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2024)

A privacy preserving querying mechanism with high utility for electric vehicles
by: Atmaca, Ugur Ilker, et al.
Published: (2022)

On the Impact of Multi-dimensional Local Differential Privacy on Fairness
by: Makhlouf, Karima, et al.
Published: (2023)

Estimating the True Distribution of Data Collected with Randomized Response
by: Pinzón, Carlos Antonio, et al.
Published: (2026)

Privacy-Preserving Federated Unlearning with Certified Client Removal
by: Liu, Ziyao, et al.
Published: (2024)

Local Differential Privacy for Federated Learning with Fixed Memory Usage and Per-Client Privacy
by: Behnia, Rouzbeh, et al.
Published: (2025)

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models
by: Nguyen, Quan, et al.
Published: (2025)

Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
by: Xu, Jiahao, et al.
Published: (2025)

Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
by: Ozturk, Ozer, et al.
Published: (2025)

Label Inference Attacks against Federated Unlearning
by: Wang, Wei, et al.
Published: (2025)

Bayes-Nash Generative Privacy Against Membership Inference Attacks
by: Zhang, Tao, et al.
Published: (2024)

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
by: Xue, Jing, et al.
Published: (2025)

Poisoning Prevention in Federated Learning and Differential Privacy via Stateful Proofs of Execution
by: Rattanavipanon, Norrathep, et al.
Published: (2024)

Privacy Against Agnostic Inference Attacks in Vertical Federated Learning
by: Varasteh, Morteza
Published: (2023)

DOPA: Stealthy and Generalizable Backdoor Attacks from a Single Client under Challenging Federated Constraints
by: Qin, Xuezheng, et al.
Published: (2025)

Comparing privacy notions for protection against reconstruction attacks in machine learning
by: Biswas, Sayan, et al.
Published: (2025)

On the Efficiency of Privacy Attacks in Federated Learning
by: Tabassum, Nawrin, et al.
Published: (2024)

Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
by: Zheng, Lele, et al.
Published: (2024)

Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation
by: Shi, Haonan, et al.
Published: (2025)

Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates
by: Wang, Chuanyin, et al.
Published: (2025)

Membership Inference Attacks and Defenses in Federated Learning: A Survey
by: Bai, Li, et al.
Published: (2024)

Convergent Differential Privacy Analysis for General Federated Learning
by: Sun, Yan, et al.
Published: (2024)

Learning-based Privacy-Preserving Graph Publishing Against Sensitive Link Inference Attacks
by: Wu, Yucheng, et al.
Published: (2025)

Towards Privacy-Preserving Split Learning: Destabilizing Adversarial Inference and Reconstruction Attacks in the Cloud
by: Higgins, Griffin, et al.
Published: (2025)

Leveraging Membership Inference Attacks for Privacy Measurement in Federated Learning for Remote Sensing Images
by: Duong, Anh-Kiet, et al.
Published: (2026)

Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
by: Ahamed, Sayyed Farid, et al.
Published: (2024)

Privacy-Preserving Federated Learning Scheme with Mitigating Model Poisoning Attacks: Vulnerabilities and Countermeasures
by: Wu, Jiahui, et al.
Published: (2025)