# Federated Unlearning

arXiv: https://arxiv.org/abs/2012.13891 · HTML: https://mlsanity.com/paper/2012.13891/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2020.

## Built on by (most cited first, 29 of 29)

- [Detecting Pretraining Data from Large Language Models](https://mlsanity.com/paper/2310.16789/) (2023)
- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [MUSE: Machine Unlearning Six-Way Evaluation for Language Models](https://mlsanity.com/paper/2407.06460/) (2024)
- [Rethinking Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2402.08787/) (2024)
- [SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning](https://mlsanity.com/paper/2404.18239/) (2024)
- [When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions](https://mlsanity.com/paper/2306.15546/) (2023)
- [VeriFi: Towards Verifiable Federated Unlearning](https://mlsanity.com/paper/2205.12709/) (2022)
- [SAFE: Machine Unlearning With Shard Graphs](https://mlsanity.com/paper/2304.13169/) (2023)
- [BLUR: A Bi-Level Optimization Approach for LLM Unlearning](https://mlsanity.com/paper/2506.08164/) (2025)
- [MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts](https://mlsanity.com/paper/2409.11844/) (2024)
- [Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy](https://mlsanity.com/paper/2410.09591/) (2024)
- [Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter](https://mlsanity.com/paper/2502.20709/) (2025)
- [A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks](https://mlsanity.com/paper/2406.06186/) (2024)
- [Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning](https://mlsanity.com/paper/2501.16663/) (2025)
- [Federated Unlearning: a Perspective of Stability and Fairness](https://mlsanity.com/paper/2402.01276/) (2024)
- [Lightweight machine unlearning in neural network](https://mlsanity.com/paper/2111.05528/) (2021)
- [Machine unlearning via GAN](https://mlsanity.com/paper/2111.11869/) (2021)
- [Forgetting Private Textual Sequences in Language Models via Leave-One-Out Ensemble](https://mlsanity.com/paper/2309.16082/) (2023)
- [Vertical Federated Unlearning via Backdoor Certification](https://mlsanity.com/paper/2412.11476/) (2024)
- [Forgettable Federated Linear Learning with Certified Data Unlearning](https://mlsanity.com/paper/2306.02216/) (2023)
- [Reinforcement Unlearning via Group Relative Policy Optimization](https://mlsanity.com/paper/2601.20568/) (2026)
- [BlockFUL: Enabling Unlearning in Blockchained Federated Learning](https://mlsanity.com/paper/2402.16294/) (2024)
- [Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2502.16708/) (2025)
- [Representation Unlearning: Forgetting through Information Compression](https://mlsanity.com/paper/2601.21564/) (2026)
- [Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning](https://mlsanity.com/paper/2605.08765/) (2026)
- [Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation](https://mlsanity.com/paper/2410.06848/) (2024)
- [Unlearning in LLMs: Methods, Evaluation, and Open Challenges](https://mlsanity.com/paper/2601.13264/) (2026)
- [MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models](https://mlsanity.com/paper/2602.23798/) (2026)
- [REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference](https://mlsanity.com/paper/2606.07141/) (2026)

## Abstract (arXiv)

Federated learning (FL) has recently emerged as a promising distributed machine learning (ML) paradigm. Practical needs of the "right to be forgotten" and countering data poisoning attacks call for efficient techniques that can remove, or unlearn, specific training data from the trained FL model. Existing unlearning techniques in the context of ML, however, are no longer in effect for FL, mainly due to the inherent distinction in the way how FL and ML learn from data. Therefore, how to enable efficient data removal from FL models remains largely under-explored. In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning methodology that can eliminate the influence of a federated client's data on the global FL model while significantly reducing the time used for constructing the unlearned FL model.The basic idea of FedEraser is to trade the central server's storage for unlearned model's construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL. A novel calibration method is further developed to calibrate the retained updates, which are further used to promptly construct the unlearned model, yielding a significant speed-up to the reconstruction of the unlearned model while maintaining the model efficacy. Experiments on four realistic datasets demonstrate the effectiveness of FedEraser, with an expected speed-up of 4× compared with retraining from the scratch. We envision our work as an early step in FL towards compliance with legal and ethical criteria in a fair and transparent manner.