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Communication Efficient and Provable Federated Unlearning
Summary: Proposes FATS, a TV-stable FedAvg variant that enables communication-efficient, provable exact federated unlearning by making the unlearned model statistically indistinguishable from retraining without the deleted data. Provides client- and sample-level unlearning algorithms with convergence/unlearning guarantees and empirical gains on 6 benchmarks.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13361
- Venue
- VLDB
- Year
- 2024
- Pagerank
- -
- Overall Rank
- 13,151 | 8.52%
- DOI
-
10.14778/3641204.3641220
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