Guaranteeing Data Privacy in Federated Unlearning with Dynamic User Participation

TL;DR

This paper proposes a privacy-preserving federated unlearning framework that integrates secure aggregation with clustering to efficiently remove users' data while protecting privacy, even with dynamic user participation.

Contribution

It introduces a novel clustering-based federated unlearning scheme that incorporates secure aggregation, addressing privacy concerns in dynamic user scenarios.

Findings

Achieves comparable unlearning effectiveness to existing methods.

Provides enhanced privacy protection against gradient leakage.

Demonstrates resilience with dynamic user participation.

Abstract

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.