# Federated Unlearning for On-Device Recommendation

arXiv: https://arxiv.org/abs/2210.10958 · HTML: https://mlsanity.com/paper/2210.10958/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2022.

## Built on by (most cited first, 30 of 43)

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- [Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics](https://mlsanity.com/paper/2401.05146/) (2024)
- [Towards Efficient and Effective Unlearning of Large Language Models for Recommendation](https://mlsanity.com/paper/2403.03536/) (2024)
- [Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning](https://mlsanity.com/paper/2304.10638/) (2023)
- [QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation](https://mlsanity.com/paper/2311.15603/) (2023)
- [Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity](https://mlsanity.com/paper/2405.17462/) (2024)
- [Federated Recommendation with Additive Personalization](https://mlsanity.com/paper/2301.09109/) (2023)
- [Machine Unlearning for Recommendation Systems: An Insight](https://mlsanity.com/paper/2401.10942/) (2024)
- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation](https://mlsanity.com/paper/2307.03363/) (2023)
- [Recommendation Unlearning via Matrix Correction](https://mlsanity.com/paper/2307.15960/) (2023)
- [Vertical Federated Unlearning via Backdoor Certification](https://mlsanity.com/paper/2412.11476/) (2024)
- [Multi-Modal Recommendation Unlearning for Legal, Licensing, and Modality Constraints](https://mlsanity.com/paper/2405.15328/) (2024)
- [Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosure](https://mlsanity.com/paper/2410.10922/) (2024)
- [How to Forget Clients in Federated Online Learning to Rank?](https://mlsanity.com/paper/2401.13410/) (2024)
- [How Secure is Forgetting? Linking Machine Unlearning to Machine Learning Attacks](https://mlsanity.com/paper/2503.20257/) (2025)
- [BlockFUL: Enabling Unlearning in Blockchained Federated Learning](https://mlsanity.com/paper/2402.16294/) (2024)
- [Goldfish: An Efficient Federated Unlearning Framework](https://mlsanity.com/paper/2404.03180/) (2024)
- [Tackling Data Heterogeneity in Federated Time Series Forecasting](https://mlsanity.com/paper/2411.15716/) (2024)
- [Unlearning for Federated Online Learning to Rank: A Reproducibility Study](https://mlsanity.com/paper/2505.12791/) (2025)
- [Exploring Federated Unlearning: Review, Comparison, and Insights](https://mlsanity.com/paper/2310.19218/) (2023)
- [ConDa: Fast Federated Unlearning with Contribution Dampening](https://mlsanity.com/paper/2410.04144/) (2024)
- [Toward Scalable Graph Unlearning: A Node Influence Maximization based Approach](https://mlsanity.com/paper/2501.11823/) (2025)
- [Model Inversion Attack against Federated Unlearning](https://mlsanity.com/paper/2502.14558/) (2025)
- [Ready2Unlearn: A Learning-Time Approach for Preparing Models with Future Unlearning Readiness](https://mlsanity.com/paper/2505.10845/) (2025)
- [FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation](https://mlsanity.com/paper/2405.13378/) (2024)
- [Peak-Controlled Logits Poisoning Attack in Federated Distillation](https://mlsanity.com/paper/2407.18039/) (2024)
- [Hypernetworks for Model-Heterogeneous Personalized Federated Learning](https://mlsanity.com/paper/2507.22330/) (2025)

## Abstract (arXiv)

The increasing data privacy concerns in recommendation systems have made federated recommendations (FedRecs) attract more and more attention. Existing FedRecs mainly focus on how to effectively and securely learn personal interests and preferences from their on-device interaction data. Still, none of them considers how to efficiently erase a user's contribution to the federated training process. We argue that such a dual setting is necessary. First, from the privacy protection perspective, ``the right to be forgotten'' requires that users have the right to withdraw their data contributions. Without the reversible ability, FedRecs risk breaking data protection regulations. On the other hand, enabling a FedRec to forget specific users can improve its robustness and resistance to malicious clients' attacks. To support user unlearning in FedRecs, we propose an efficient unlearning method FRU (Federated Recommendation Unlearning), inspired by the log-based rollback mechanism of transactions in database management systems. It removes a user's contribution by rolling back and calibrating the historical parameter updates and then uses these updates to speed up federated recommender reconstruction. However, storing all historical parameter updates on resource-constrained personal devices is challenging and even infeasible. In light of this challenge, we propose a small-sized negative sampling method to reduce the number of item embedding updates and an importance-based update selection mechanism to store only important model updates. To evaluate the effectiveness of FRU, we propose an attack method to disturb FedRecs via a group of compromised users and use FRU to recover recommenders by eliminating these users' influence. Finally, we conduct experiments on two real-world recommendation datasets with two widely used FedRecs to show the efficiency and effectiveness of our proposed approaches.