Federated Unlearning

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Federated unlearning studies how to remove the influence of clients or individual records from models trained across distributed data, without simply rebuilding the model from scratch. Approaches include retraining and replay, influence- or gradient-based updates, exact deletion from additive statistics, and lightweight methods such as pruning, filters, distillation, or proxy-data calibration. Methods are tested in settings including non-IID image and medical data, graphs, personalized models, multimodal systems, and federated language models, often against retraining as a reference. A central tension is making deletion fast and preserving utility while ensuring forgotten information is truly gone: output accuracy alone may miss residual representations or membership signals, and server-side summaries can themselves leak data.

Latest papers

29 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Split Federated Unlearning
Oct 27, 2026Jia Chen, Yecheng Lv, Xinrong Hu +2Federated Unlearning Split Federated Learning
School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China · Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University, Wuhan, China · Australian Institute for Machine Learning (AIML), Adelaide University, Adelaide, Australia
One-Shot Federated Model Editing for Device Dynamics at the Edge
Oct 26, 2026Youming Tao, Weijun Yin, Huanyi Xie +5Federated Learning Federated Unlearning
School of Electrical Engineering and Computer Science, TU Berlin, 10623 Berlin, Germany · School of Cyber Science and Technology, Beihang University, Beijing 100191, China · Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100083, China +2
Efficient Exact Unlearning for Federated Zeroth-Order Optimization
Oct 26, 2026Weijun Yin, Yanqing Yao, Falko Dressler +2Federated Unlearning Federated Learning
School of Cyber Science and Technology, Beihang University, Beijing 100191, China · Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100083, China · School of Electrical Engineering and Computer Science, TU Berlin, 10623 Berlin, Germany +1
Not All Retained Clients Are Equal: Client Selection for Federated Unlearning
Oct 22, 2026Thanh Linh Nguyen, Quoc-Viet PhamFederated Unlearning Client Selection in Federated Learning
School of Computer Science and Statistics, Trinity College Dublin, Ireland
Initiating Removal from Operational Symptoms in Federated Vehicular Networks
Oct 15, 2026Zihao Ding, Jun Huang, Qiang DuanFederated Unlearning Federated Learning
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA · Information Sciences and Technology Department, Pennsylvania State University, Abington, PA 19001, USA
FIRM: Fisher-Informed Retention Merging for Federated LLM Unlearning
Oct 15, 2026Reeyansh Shah, Maram Hasan, Subhasis Chaudhuri +1Federated Unlearning Machine Unlearning
Indian Institute of Technology Bombay
Machine Unlearning for Federated Socially Fair k-Means Clustering
Oct 9, 2026Aravind Reddy, Deeraj S K, Mohd Wasif Raza +1Clustering Federated Unlearning
Centre for Responsible AI, Wadhwani School of Data Science & AI, Indian Institute of Technology Madras · Department of Artificial Intelligence, Sardar Vallabhbhai National Institute of Technology, Surat, India · Department of Electrical Engineering, Indian Institute of Technology Madras
Client-Side Probing of Deleted Ridge Statistics in Federated Unlearning
Sep 3, 2026Yijun Quan, Giovanni MontanaTraining Data Reconstruction Attacks Federated Unlearning
University of Warwick
Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction
Aug 17, 2026Aditya Kumar, Sumit ChongderQuantum Federated Learning Federated Unlearning
IDRP - Quantum Information and Computation, Department of Physics & Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan 342030, India
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 · Electrical & Computer Engineering, Baylor University, Waco, USA
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
PrivEraserVerify: Efficient, Private, and Verifiable Federated Unlearning
Apr 14, 2026Parthaw Goswami, Md Khairul Islam, Ashfak YeafiFederated Unlearning Privacy-Preserving Federated Learning
Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh · Department of Mathematics and Computer Science, Hobart and William Smith Colleges, Geneva, NY, USA · Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh
Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
Apr 6, 2026Houzhe Wang, Xiaojie Zhu, Chi ChenFederated Unlearning Machine Unlearning Evaluation
School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement
Apr 5, 2026Houzhe Wang, Xiaojie Zhu, Chi ChenFederated Unlearning
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression
Mar 14, 2026Minh-Duong Nguyen, Senura Hansaja, Le-Tuan Nguyen +4Federated Unlearning Federated Learning
VinUniversity · University of Sydney · Trinity College Dublin
Exact Federated Continual Unlearning for Ridge Heads on Frozen Foundation Models
Mar 13, 2026Yijun Quan, Wentai Wu, Giovanni MontanaFederated Unlearning
WMG, University of Warwick, Coventry CV4 7AL, UK · Department of Computer Science, Jinan University, Guangzhou 510632, China

Oct 27, 2026J/K move · Enter open · S save

Split Federated Unlearning

Jia Chen, Yecheng Lv, Xinrong Hu, Mingdi Huang+1

School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China · Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University, Wuhan, China · Australian Institute for Machine Learning (AIML), Adelaide University, Adelaide, Australia

Federated Unlearning Split Federated Learning

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Split federated learning (SFL) combines partitioned model execution with federated aggregation to support collaborative learning across distributed Web and edge environments. However, removing learned contributions in SFL is challenging due to cross-partition coupling and historical influence propagation through dual-side aggregation. To the best of our knowledge, we provide the first systematic formulation of Split Federated Unlearning (SFU), encompassing client-wise and class-wise removal, counterfactual retraining, and partition-specific update constraints. We theoretically characterize historical influence propagation and mode-dependent unlearning feasibility, revealing structural limitations of restricted model updates. As an initial solution, we propose SURE, integrating dual-importance estimation, selective parameter perturbation, and importance-guided recovery. Extensive experiments with existing unlearning methods adapted to SFL reveal substantial forgetting-utility trade-offs across model architectures, data distributions, and update modes. SURE achieves competitive agreement with mode-matched retraining at modest additional cost, while further analyses highlight the impact of split position and update constraints on unlearning behavior. These findings establish SFU as a distinct research problem and provide a foundation for unlearning in partitioned federated systems.