---
title: Federated Unlearning for FL Systems
url: https://www.emergentmind.com/topics/federated-unlearning-fu
type: topic
---

# Federated Unlearning for FL Systems

Federated Unlearning (FU) is a collection of methodologies and protocols developed to enable the selective removal of data—at the sample, class, or client level—from models trained in distributed federated learning (FL) systems. FU formalizes the “right to be forgotten” in FL contexts, where a central server and multiple clients collaboratively train a model without exchanging raw data, by allowing clients to request that previously contributed data be “forgotten” such that the resulting model is statistically indistinguishable from one retrained from scratch on the remaining data [2310.19218]. This capability is central to compliance with data-protection regulations such as GDPR and the California CCPA, as well as for responding to poisoning backdoors, fairness correction, and removal of sensitive features. FU faces unique technical, system, and regulatory challenges due to the privacy-preserving and distributed nature of FL.

## 1. Foundations and Formal Definitions

Federated Unlearning generalizes the notion of machine unlearning from centralized machine learning to FL, addressing the distributed data setting. The central goal is, given a global model $\theta$ trained including data $S$ (to be unlearned), to produce an updated global model $\theta^u$ such that
$$
\Pr(M_{\theta^*}( \mathcal{D} \setminus S )) \approx \Pr(M_{\theta^u}( \mathcal{D} )),
$$
where $\theta^*$ is trained from scratch without $S$ and $\theta^u$ is generated by an efficient FU protocol [2310.19218]. FU requests can be made at multiple granularities:

- **Client-Level**: Completely removing one or more clients' contributions.
- **Sample-Level**: Erasing specific data points within a client's local dataset.
- **Class-Level**: Targeting all examples of a particular class system-wide.

In the standard FL context, $K$ clients each hold local datasets $\mathcal{D}_k$, and collaboratively update the global model via rounds in which the server broadcasts parameters, clients perform local updates, and then the server aggregates updates [2310.19218, 2401.05146].

The design and evaluation of FU methods center on three primary dimensions:

- **Privacy/Unlearning Guarantee**: Quantified by an $\epsilon$-unlearning guarantee; ideally, the log-likelihood ratio between pre- and post-unlearning models is bounded by $\epsilon$.
- **Accuracy/Utility**: The difference in test accuracy or task loss before and after unlearning, $\Delta A = A_\text{before} - A_\text{after}$.
- **Efficiency**: Computational, storage, and communication overhead compared to (re)training from scratch [2310.19218, 2401.05146].

## 2. Taxonomy of Federated Unlearning Methodologies

FU strategies

Source: https://www.emergentmind.com/topics/federated-unlearning-fu