Federated unlearning using diffusive noise injection

Recent advances in machine learning and deep learning have transformed daily life by using user data to extract patterns and insights. As data privacy concerns rise, the “right to be forgotten” has become increasingly important, driving the development of machine unlearning–a technique to remove specific data contributions from trained models. Most existing unlearning research assumes a centralized setting where the data resides on a central server. However, this assumption breaks in federated learning (FL), where data remains decentralized across clients who train a shared model without exposing raw data. This decentralized architecture introduces significant challenges for unlearning, such as identifying and removing specific data contributions, preserving global model performance, and ensuring privacy. Addressing these issues, we propose a client-level machine unlearning framework based on Diffusive Noise Injection (DNI). DNI gradually perturbs training inputs with structured noise to steer the model away from memorizing specific samples or classes, followed by a global model healing phase to restore accuracy and stability. The proposed approach is evaluated using Convolutional Neural Networks (CNNs) and Vision Transformers on standard FL benchmarks including CIFAR-10, CIFAR-100, and MNIST, as well as the KVASIR medical image dataset. Experimental results show that our method effectively unlearns target data while maintaining high accuracy, achieving performance comparable to state-of-the-art unlearning techniques across all datasets and model architectures.