# Strategic Data Revocation in Federated Unlearning

arXiv: https://arxiv.org/abs/2312.01235 · HTML: https://mlsanity.com/paper/2312.01235/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2023.

## Built on by (most cited first, 7 of 7)

- [Threats, Attacks, and Defenses in Machine Unlearning: A Survey](https://mlsanity.com/paper/2403.13682/) (2024)
- [Privacy-Preserving Federated Unlearning with Certified Client Removal](https://mlsanity.com/paper/2404.09724/) (2024)
- [Beyond Right to be Forgotten: Managing Heterogeneity Side Effects Through Strategic Incentives](https://mlsanity.com/paper/2410.15045/) (2024)
- [Upcycling Noise for Federated Unlearning](https://mlsanity.com/paper/2412.05529/) (2024)
- [Dual-Phase Federated Deep Unlearning via Weight-Aware Rollback and Reconstruction](https://mlsanity.com/paper/2512.13381/) (2025)
- [Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression](https://mlsanity.com/paper/2603.13795/) (2026)
- [Client-Verifiable and Efficient Federated Unlearning in Low-Altitude Wireless Networks](https://mlsanity.com/paper/2603.29688/) (2026)

## Abstract (arXiv)

By allowing users to erase their data's impact on federated learning models, federated unlearning protects users' right to be forgotten and data privacy. Despite a burgeoning body of research on federated unlearning's technical feasibility, there is a paucity of literature investigating the considerations behind users' requests for data revocation. This paper proposes a non-cooperative game framework to study users' data revocation strategies in federated unlearning. We prove the existence of a Nash equilibrium. However, users' best response strategies are coupled via model performance and unlearning costs, which makes the equilibrium computation challenging. We obtain the Nash equilibrium by establishing its equivalence with a much simpler auxiliary optimization problem. We also summarize users' multi-dimensional attributes into a single-dimensional metric and derive the closed-form characterization of an equilibrium, when users' unlearning costs are negligible. Moreover, we compare the cases of allowing and forbidding partial data revocation in federated unlearning. Interestingly, the results reveal that allowing partial revocation does not necessarily increase users' data contributions or payoffs due to the game structure. Additionally, we demonstrate that positive externalities may exist between users' data revocation decisions when users incur unlearning costs, while this is not the case when their unlearning costs are negligible.