将联邦去学习视为参数估计问题
Tackling Federated Unlearning as a Parameter Estimation Problem
AI总结：
本文提出了一种基于信息论的联邦去学习框架，通过参数估计方法高效实现数据遗忘，提升隐私保护和模型性能。
摘要：
隐私法规要求从深度学习模型中删除数据。这是在联邦学习中尤为显著的挑战，因为数据仍保留在客户端上，使得完全重新训练或协调更新往往不可行。本文介绍了一种基于信息论的高效联邦去学习框架，将泄漏视为参数估计问题。我们的方法利用二阶Hessian信息来识别并选择性地重置对被遗忘数据最敏感的参数，随后进行最小的联邦重新训练。这种模型无关的方法支持分类和客户端去学习，无需在初始信息聚合后服务器访问原始客户端数据。在基准数据集上的评估显示，该方法在隐私方面表现优异（成员检测成功率接近随机，分类知识被擦除），在性能方面也表现出色（归一化准确性相对于重新训练基准约为0.9），同时在完全重新训练的情况下提高了效率。此外，在针对后门攻击的场景中，我们的框架有效中和了恶意触发器，恢复了模型完整性。这为联邦学习中的数据遗忘提供了实用的解决方案。
英文摘要：
Privacy regulations require the erasure of data from deep learning models. This is a significant challenge that is amplified in Federated Learning, where data remains on clients, making full retraining or coordinated updates often infeasible. This work introduces an efficient Federated Unlearning framework based on information theory, modeling leakage as a parameter estimation problem. Our method uses second-order Hessian information to identify and selectively reset only the parameters most sensitive to the data being forgotten, followed by minimal federated retraining. This model-agnostic approach supports categorical and client unlearning without requiring server access to raw client data after initial information aggregation. Evaluations on benchmark datasets demonstrate strong privacy (MIA success near random, categorical knowledge erased) and high performance (Normalized Accuracy against re-trained benchmarks of $\approx$ 0.9), while aiming for increased efficiency over complete retraining. Furthermore, in a targeted backdoor attack scenario, our framework effectively neutralizes the malicious trigger, restoring model integrity. This offers a practical solution for data forgetting in FL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
