Communication Efficient and Provable Federated Unlearning
Summary: Introduces exact federated unlearning with retraining-equivalent, provable guarantees for deleted clients or samples. FATS adds total-variation stability to FedAvg, enabling communication-efficient unlearning. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Youming Tao (Shandong University)
- 2. Cheng-Long Wang (King Abdullah University of Science and Technology)
- 3. Miao Pan (University of Houston)
- 4. Dongxiao Yu (Shandong University)
- 5. Xiuzhen Cheng (Shandong University)
- 6. Di Wang (King Abdullah University of Science and Technology)
BibTeX Citation
@article{tao_vldb24,
title = {{Communication Efficient and Provable Federated Unlearning}},
author = {Tao, Youming and Wang, Cheng-Long and Pan, Miao and Yu, Dongxiao and Cheng, Xiuzhen and Wang, Di},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {5},
pages = {1119--1131},
doi = {10.14778/3641204.3641220},
url = {https://doi.org/10.14778/3641204.3641220},
year = {2024}
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