用参数估计方法高效实现联邦学习中的数据遗忘。
Tackling Federated Unlearning as a Parameter Estimation Problem
- 将遗忘数据视为参数估计问题，利用海森矩阵识别敏感参数。
- 仅重置关键参数，再经少量联邦训练，隐私泄露率接近随机水平。
- 无需服务器访问原始数据，适合真实场景的隐私保护需求。
隐私法规要求从深度学习模型中擦除数据，这在联邦学习中尤为困难，因数据保留在客户端，全量重训或协同更新常不可行。本文提出一种基于信息论的高效联邦遗忘框架，将数据泄漏建模为参数估计问题。方法利用二阶海森信息识别对被遗忘数据最敏感的参数，仅选择性重置这些参数，随后进行最小化联邦重训。该模型无关方法支持类别级和客户端级遗忘，且无需服务器在初始信息聚合后访问原始客户端数据。基准数据集评估显示，该方法具有强隐私保护能力（成员推断攻击成功率接近随机），性能优异（与重新训练基准相比归一化准确率约0.9），同时显著提升效率。此外，在定向后门攻击场景下，该框架能有效消除恶意触发器，恢复模型完整性，为联邦学习中的数据遗忘提供了实用解决方案。
原文摘要 · Abstract (English)
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.
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