Federated Unlearning

Follow this topic Ask about this topic

Momentum

0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 15

All topics

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging
Aug 2, 2026Shengchao Chen, Ting ShuFederated Unlearning Medical Image Benchmarks
University of Technology Sydney · Shenzhen University
When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks
Jul 30, 2026Zihao Ding, Jun Huang, Liang DongFederated Unlearning Federated Continual Learning
Electrical Engr & Computer Science · South Dakota State University · Brookings, USA +3
MMFGU: Multimodal Federated Graph Unlearning
Jul 30, 2026Haodong Lu, Zekai Chen, Weiwei Ji +5Multimodal Federated Learning Federated Unlearning
Beijing Institute of Technology Beijing, China
FedUP: One-Shot Federated Unlearning via Centroid-Guided Plug-in Filters
Jun 23, 2026Feihong Nan, Zhengyi Zhong, Pan Wang +4Federated Unlearning Machine Unlearning
1National Key Laboratory of Big Data and Decision, National University of Defense Technology, China.
Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning
Jun 22, 2026Nuocheng Yang, Yechen He, Sihua Wang +3Federated Unlearning Communication-Efficient Distributed Training
Beijing Laboratory of Advanced Information Network, and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China · Information Systems Technology and Design Pillar, Singapore University of Technology and Design, 487372, Singapore
pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning
Jun 15, 2026Zhuodong Liu, Xiangyu Li, Zhihao ZhangFederated Unlearning Federated Learning
1Saunders College of Business, Rochester Institute of Technology, Rochester, NY 14623, USA · School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Fed-FBD: Federated Functional Block Diversification for Isolation, Privacy, and Surgical Unlearning
Jun 10, 2026Weijie Chen, Alan B. McMillanFederated Unlearning Byzantine-Robust Federated Learning
Department of Radiology, University of Wisconsin–Madison
Image Feature Fusion-based Federated Client Unlearning (FCU)
May 26, 2026Hangyi Shen, Yizhi Pan, Tiansuo Li +2Federated Unlearning Mixup
11Hangzhou Medical College, Hangzhou, China
Rethinking Federated Unlearning via the Lens of Memorization
May 23, 2026Jiaheng Wei, Yanjun Zhang, He Zhang +5Federated Unlearning Neural Network Memorization
Royal Melbourne Institute of Technology · Griffith University · Swinburne University of Technology
Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions
May 19, 2026Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi +1Federated Unlearning Krylov Subspace Methods
Department of Computer Engineering SRC, Islamic Azad University Tehran, Iran · School of Computer Engineering Iran University of Science and Technology Tehran, Iran · Meta CA, USA
Do Vision Models Truly Forget? New Findings from Representation-Level Certification of Visual Unlearning in Vertical Federated Learning
May 19, 2026Zhenyu Yu, Yangchen Zeng, Chunlei Meng +2Federated Unlearning Machine Unlearning
College of Computer Science and Artificial Intelligence, Fudan University · School of Cyber Science and Engineering, Southeast University · College of Intelligent Robotics and Advanced Manufacturing, Fudan University +1
Graph Federated Unlearning for Privacy Preservation
May 4, 2026Ruotong Ma, Wentao Yu, Qizhou Wang +2Federated Unlearning Federated Graph Learning
Shanghai Jiao Tong University · Nanjing University of Science and Technology · Hong Kong Baptist University China
Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
May 4, 2026Wenwei Zhao, Xiaowen Li, Yao Liu +1Federated Unlearning Machine Unlearning
Department of Electrical Engineering, University of South Florida,Tampa, FL, USA · Bellini College, University of South Florida, Tampa, FL, USA
EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure
May 1, 2026Zihao Ding, Beining Wu, Jun HuangCross-Modal Learning Multimodal Federated Learning
Department of Electrical Engineering and Computer Science South Dakota State University Brookings, SD 57007, USA
Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
Apr 29, 2026Zhaoyuan Cai, Xinglin ZhangFederated Unlearning Medical Imaging
School of Computer Science and Engineering South China University of Technology Guangzhou, China

Aug 2, 2026, cs.CVJ/K move · Enter open · S save

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging

Shengchao Chen, Ting Shu

University of Technology Sydney · Shenzhen University

Federated Unlearning Medical Image Benchmarks Federated Learning

Open paper AskPDF arXiv

Federated learning enables medical-imaging models to be trained across hospitals, and privacy law, most explicitly the GDPR ``right to be forgotten'', turns removing a hospital's, a class's, or a patient's influence from such a model into a federated unlearning problem. This need is most acute in medicine, where patients withdraw consent and hospitals leave collaborations. Yet nearly all unlearning evidence comes from natural images, whose heterogeneity and task structure differ sharply from clinical data, so it is unclear whether existing methods transfer, and no shared protocol covers clinical data. We present Lethe, a benchmark for federated unlearning in medical imaging. It evaluates twelve methods across eight task families, from classification and segmentation to denoising, cross-modality synthesis, and vision-language question answering, at three forgetting granularities and against a retrained gold standard on utility, privacy, and cost. The central result is that what separates methods is the difficulty of the forgetting request, not the method itself. The easy removals that dominate the literature leave the methods that preserve utility indistinguishable, while only hard ones separate them. More striking, on the many medical tasks that generalize across sites, forgetting a client barely changes task performance, leaving residual membership as the signal that must be erased.

Momentum

Related topics

Latest papers 15