Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, copyright, and safety requirements. In real deployments, providers distribute a global model to many edge devices, where each client personalizes the model using private data. When a deletion request is issued, clients may ignore it or falsely claim compliance, and providers cannot check their parameters or data. This makes verification difficult, especially because personalized models must forget the targeted samples while preserving local utility, and verification must remain lightweight on edge devices. We introduce ZK APEX, a zero-shot personalized unlearning method that operates directly on the personalized model without retraining. ZK APEX combines sparse masking on the provider side with a small Group OBS compensation step on the client side, using a blockwise empirical Fisher matrix to create a curvature-aware update designed for low overhead. Paired with Halo2 zero-knowledge proofs, it enables the provider to verify that the correct unlearning transformation was applied without revealing any private data or personalized parameters. On Vision Transformer classification tasks, ZK APEX recovers nearly all personalization accuracy while effectively removing the targeted information. Applied to the OPT125M generative model trained on code data, it recovers around seventy percent of the original accuracy. Proof generation for the ViT case completes in about two hours, more than ten million times faster than retraining-based checks, with less than one gigabyte of memory use and proof sizes around four hundred megabytes. These results show the first practical framework for verifiable personalized unlearning on edge devices.

翻译：机器学习遗忘旨在从训练好的模型中移除特定数据点的影响，以满足隐私、版权和安全需求。在实际部署中，服务提供商将全局模型分发给多个边缘设备，每个客户端利用私有数据对模型进行个性化。当收到删除请求时，客户端可能忽略该请求或虚假声称已合规，而服务提供商无法检查其参数或数据。这使得验证变得困难，特别是因为个性化模型必须在保留本地效用的同时遗忘目标样本，且验证过程必须在边缘设备上保持轻量级。我们提出了ZK APEX，一种零样本个性化遗忘方法，可直接在个性化模型上操作而无需重新训练。ZK APEX结合了服务提供商端的稀疏掩码与客户端的小型Group OBS补偿步骤，利用分块经验Fisher矩阵创建了一种针对低开销设计的曲率感知更新。配合Halo2零知识证明，该方法使服务提供商能够验证正确的遗忘变换已应用，而无需暴露任何私有数据或个性化参数。在Vision Transformer分类任务中，ZK APEX在有效移除目标信息的同时恢复了几乎全部个性化准确率。应用于基于代码数据训练的OPT125M生成模型时，该方法恢复了约70%的原始准确率。在ViT案例中，证明生成耗时约两小时，比基于重新训练的验证快超过一千万倍，内存使用量低于1GB，证明大小约为400MB。这些结果展示了首个在边缘设备上可验证个性化遗忘的实用框架。