The right to be forgotten is a fundamental principle of privacy-preserving regulations and extends to Machine Learning (ML) paradigms such as Federated Learning (FL). While FL enhances privacy by enabling collaborative model training without sharing private data, trained models still retain the influence of training data. Federated Unlearning (FU) methods recently proposed often rely on impractical assumptions for real-world FL deployments, such as storing client update histories or requiring access to a publicly available dataset. To address these constraints, this paper introduces a novel method that leverages negated Pseudo-gradients Updates for Federated Unlearning (PUF). Our approach only uses standard client model updates, which are employed during regular FL rounds, and interprets them as pseudo-gradients. When a client needs to be forgotten, we apply the negation of their pseudo-gradients, appropriately scaled, to the global model. Unlike state-of-the-art mechanisms, PUF seamlessly integrates with FL workflows, incurs no additional computational and communication overhead beyond standard FL rounds, and supports concurrent unlearning requests. We extensively evaluated the proposed method on two well-known benchmark image classification datasets (CIFAR-10 and CIFAR-100) and a real-world medical imaging dataset for segmentation (ProstateMRI), using three different neural architectures: two residual networks and a vision transformer. The experimental results across various settings demonstrate that PUF achieves state-of-the-art forgetting effectiveness and recovery time, without relying on any additional assumptions.

翻译：被遗忘权是隐私保护法规的基本原则，并延伸至联邦学习（FL）等机器学习（ML）范式。尽管联邦学习通过在不共享私有数据的情况下实现协作模型训练来增强隐私，但训练后的模型仍保留训练数据的影响。近期提出的联邦遗忘（FU）方法通常依赖于不切实际的假设，例如存储客户端更新历史或要求访问公开可用数据集，这在实际联邦学习部署中难以实现。为解决这些限制，本文提出一种新颖方法，利用负伪梯度更新进行联邦遗忘（PUF）。我们的方法仅使用常规联邦学习轮次中采用的标准客户端模型更新，并将其解释为伪梯度。当需要遗忘某个客户端时，我们将其伪梯度的负值（经适当缩放）应用于全局模型。与现有先进机制不同，PUF能无缝集成到联邦学习工作流中，除标准联邦学习轮次外不产生额外的计算和通信开销，并支持并发遗忘请求。我们在两个知名基准图像分类数据集（CIFAR-10和CIFAR-100）和一个真实世界医学影像分割数据集（ProstateMRI）上，使用三种不同神经架构（两个残差网络和一个视觉Transformer）对所提方法进行了全面评估。多种设置下的实验结果表明，PUF在不依赖任何额外假设的情况下，实现了最先进的遗忘效果和恢复时间。