Cover Image

Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cai, Zhaoyuan, Zhang, Xinglin
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Orthogonal Calibration for Asynchronous Federated Learning
by: Zhang, Jiayun, et al.
Published: (2025)

Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging
by: Deng, Zhipeng, et al.
Published: (2024)

Machine Unlearning for Medical Imaging
by: Nasirigerdeh, Reza, et al.
Published: (2024)

Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning
by: Wang, Changsheng, et al.
Published: (2025)

Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging
by: Zheng, Guangyao, et al.
Published: (2023)

Federated Graph Unlearning
by: Ai, Yuming, et al.
Published: (2025)

SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
by: Fraboni, Yann, et al.
Published: (2022)

Pure Exploration in Asynchronous Federated Bandits
by: Wang, Zichen, et al.
Published: (2023)

Vertical Federated Unlearning via Backdoor Certification
by: Han, Mengde, et al.
Published: (2024)

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient
by: Zhou, Lei, et al.
Published: (2024)

Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter
by: Zhong, Zhengyi, et al.
Published: (2025)

Targeted Unlearning with Single Layer Unlearning Gradient
by: Cai, Zikui, et al.
Published: (2024)

Certified Unlearning in Decentralized Federated Learning
by: Wu, Hengliang, et al.
Published: (2026)

Graph Federated Unlearning for Privacy Preservation
by: Ma, Ruotong, et al.
Published: (2026)

Asynchronous Probability Ensembling for Federated Disaster Detection
by: Martins, Emanuel Teixeira, et al.
Published: (2026)

Momentum Approximation in Asynchronous Private Federated Learning
by: Yu, Tao, et al.
Published: (2024)

Aggregation on Learnable Manifolds for Asynchronous Federated Optimization
by: Licudi, Archie, et al.
Published: (2025)

Machine Unlearning via Null Space Calibration
by: Chen, Huiqiang, et al.
Published: (2024)

Federated Unlearning for Human Activity Recognition
by: Chen, Kongyang, et al.
Published: (2024)

DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion Models
by: Yu, Zhengyang, et al.
Published: (2024)

Federated Unlearning Model Recovery in Data with Skewed Label Distributions
by: Yu, Xinrui, et al.
Published: (2024)

FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness
by: Wen, Siyuan, et al.
Published: (2025)

Unlearning during Learning: An Efficient Federated Machine Unlearning Method
by: Gu, Hanlin, et al.
Published: (2024)

Exploring Federated Unlearning: Review, Comparison, and Insights
by: Zhao, Yang, et al.
Published: (2023)

Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification
by: Yan, Jintao, et al.
Published: (2025)

Asynchronous Federated Clustering with Unknown Number of Clusters
by: Zhang, Yunfan, et al.
Published: (2024)

Federated Unlearning in the Wild: Rethinking Fairness and Data Discrepancy
by: Huang, ZiHeng, et al.
Published: (2025)

Goldfish: An Efficient Federated Unlearning Framework
by: Wang, Houzhe, et al.
Published: (2024)

Blockchain-enabled Trustworthy Federated Unlearning
by: Lin, Yijing, et al.
Published: (2024)

Benchmarking Federated Machine Unlearning methods for Tabular Data
by: Xiao, Chenguang, et al.
Published: (2025)

DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning
by: Han, Yizhou, et al.
Published: (2026)

TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation
by: Kirscher, Tristan, et al.
Published: (2026)

Scalable Asynchronous Federated Modeling for Spatial Data
by: Shi, Jianwei, et al.
Published: (2025)

Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images
by: Nahass, George R., et al.
Published: (2025)

Deep Unlearn: Benchmarking Machine Unlearning for Image Classification
by: Cadet, Xavier F., et al.
Published: (2024)

DAG-AFL:Directed Acyclic Graph-based Asynchronous Federated Learning
by: Zhang, Shuaipeng, et al.
Published: (2025)

Asynchronous Personalized Federated Learning through Global Memorization
by: Wan, Fan, et al.
Published: (2025)

PrivEraserVerify: Efficient, Private, and Verifiable Federated Unlearning
by: Goswami, Parthaw, et al.
Published: (2026)

Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
by: Gu, Hanlin, et al.
Published: (2024)

Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems
by: Gao, Jikun, et al.
Published: (2024)