# Guaranteeing Data Privacy in Federated Unlearning with Dynamic User Participation

arXiv: https://arxiv.org/abs/2406.00966 · HTML: https://mlsanity.com/paper/2406.00966/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [FROC: A Unified Framework with Risk-Optimized Control for Machine Unlearning in LLMs](https://mlsanity.com/paper/2512.13337/) (2025)
- [Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning](https://mlsanity.com/paper/2512.23171/) (2025)
- [Beyond Right to be Forgotten: Managing Heterogeneity Side Effects Through Strategic Incentives](https://mlsanity.com/paper/2410.15045/) (2024)
- [DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences](https://mlsanity.com/paper/2507.09602/) (2025)

## Abstract (arXiv)

Federated Unlearning (FU) is gaining prominence for its capability to eliminate influences of Federated Learning (FL) users' data from trained global FL models. A straightforward FU method involves removing the unlearned users and subsequently retraining a new global FL model from scratch with all remaining users, a process that leads to considerable overhead. To enhance unlearning efficiency, a widely adopted strategy employs clustering, dividing FL users into clusters, with each cluster maintaining its own FL model. The final inference is then determined by aggregating the majority vote from the inferences of these sub-models. This method confines unlearning processes to individual clusters for removing a user, thereby enhancing unlearning efficiency by eliminating the need for participation from all remaining users. However, current clustering-based FU schemes mainly concentrate on refining clustering to boost unlearning efficiency but overlook the potential information leakage from FL users' gradients, a privacy concern that has been extensively studied. Typically, integrating secure aggregation (SecAgg) schemes within each cluster can facilitate a privacy-preserving FU. Nevertheless, crafting a clustering methodology that seamlessly incorporates SecAgg schemes is challenging, particularly in scenarios involving adversarial users and dynamic users. In this connection, we systematically explore the integration of SecAgg protocols within the most widely used federated unlearning scheme, which is based on clustering, to establish a privacy-preserving FU framework, aimed at ensuring privacy while effectively managing dynamic user participation. Comprehensive theoretical assessments and experimental results show that our proposed scheme achieves comparable unlearning effectiveness, alongside offering improved privacy protection and resilience in the face of varying user participation.