FedCIO: efficient exact federated unlearning with clustering, isolation, and one-shot aggregation
Data are invaluable in machine learning (ML), yet they raise significant privacy concerns. In the real world, data are often distributed across isolated silos, challenging conventional ML methods that centralize data. Federated learning (FL) offers a privacy-preserving solution that enables learning...
Saved in:
Main Authors: | Qiu, Hongyu, Wang, Yongwei, Xu, Yonghui, Cui, Lizhen, Shen, Zhiqi |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/173926 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
A survey on federated unlearning: challenges, methods, and future directions
by: Liu, Ziyao, et al.
Published: (2025) -
Semantic deep hiding for robust unlearnable examples
by: Meng, Ruohan, et al.
Published: (2024) -
Unlearnable example with face images
by: Peng, Haohang
Published: (2025) -
Data protection with unlearnable examples
by: Ma, Xiaoyu
Published: (2024) -
Efficient asynchronous multi-participant vertical federated learning
by: Shi, Haoran, et al.
Published: (2024)