federated-unlearning

Awesome Machine Unlearning (A Survey of Machine Unlearning)

Awesome Federated Unlearning (FU) Papers (Continually Update)

[NeurIPS 2024] Official implementation of the paper “Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity"

An Empirical Study of Federated Unlearning: Efficiency and Effectiveness (Accepted Conference Track Papers at ACML 2023)

Papers related to Machine/ Federated Unlearning in all top venues

[ICASSP 2026] "Top-1 compression suffices for federated unlearning with the help of adaptive error feedback" by Boxu Xiao, Sijia Liu, Qing Ling

A continuously-updated catalog of machine unlearning papers with live citation counts.

Federated unlearning through functional encryption.

ConDa is an efficient federated unlearning framework that removes a client's data from a global model without retraining or additional computational overhead. It outperforms existing methods by at least 100× in non-IID federated learning settings.

A comprehensive framework simulating integrity (Backdoor) and privacy (Gradient Inversion) attacks in Federated Learning, featuring a Machine Unlearning defense strategy

Federated Unlearning framework for medical imaging using FedEraser, enabling efficient removal of client-specific data from trained models without full retraining.

Implementation of Federated Unlearning for medical image classification using the FedEraser approach. Demonstrates how client data contributions can be removed from a trained federated learning model without full retraining.

Provably Convergent Federated Unlearning with the Aid of Retained Gradient Estimators

MGA: Mirror Gradient Ascent for Asynchronous Federated Unlearning - staleness-aware reference modeling + constrained reverse optimization for efficient async federated unlearning (PyTorch).