# Exploring Federated Unlearning: Review, Comparison, and Insights

arXiv: https://arxiv.org/abs/2310.19218 · HTML: https://mlsanity.com/paper/2310.19218/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security.
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Placed in

- LLM safety and security / [Federated unlearning and poisoning](https://mlsanity.com/safety/federated-unlearning-and-poisoning/)

## Builds on (most cited first, 3 of 3)

- [Federated Unlearning via Class-Discriminative Pruning](https://mlsanity.com/paper/2110.11794/) (2021)
- [Federated Unlearning for On-Device Recommendation](https://mlsanity.com/paper/2210.10958/) (2022)
- [Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning](https://mlsanity.com/paper/2302.02069/) (2023)

## Built on by (most cited first, 2 of 2)

- [Storage Is Not Strategy: State-Conditioned Support Control for LLM Unlearning](https://mlsanity.com/paper/2609.37858/) (2026)
- [On Unlearning for Time-series Forecasting](https://mlsanity.com/paper/2610.02865/) (2026)

## Abstract (arXiv)

The increasing demand for privacy-preserving machine learning has spurred interest in federated unlearning, which enables the selective removal of data from models trained in federated systems. However, developing federated unlearning methods presents challenges, particularly in balancing three often conflicting objectives: privacy, accuracy, and efficiency. This paper provides a comprehensive analysis of existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy. We discuss key trade-offs among these dimensions and highlight their implications for practical applications across various domains. Additionally, we propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods, incorporating classic baselines and diverse performance metrics. Our findings aim to guide practitioners in navigating the complex interplay of these objectives, offering insights to achieve effective and efficient federated unlearning. Finally, we outline directions for future research to further advance the state of federated unlearning techniques.