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.

翻译：隐私法规要求从深度学习模型中删除数据。这在联邦学习中是一个重大挑战，因为数据保留在客户端，使得完全重新训练或协调更新通常不可行。本文基于信息论提出了一种高效的联邦遗忘框架，将信息泄露建模为参数估计问题。我们的方法利用二阶Hessian信息来识别并选择性重置对遗忘数据最敏感的参量，随后进行最小化联邦再训练。这种与模型无关的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。在基准数据集上的评估表明，该方法具有强隐私性（成员推理攻击成功率接近随机猜测，类别知识被有效擦除）和高性能（相对于重新训练基准的归一化准确率约0.9），同时致力于提升完全重新训练的效率。此外，在针对性后门攻击场景中，本框架能有效消除恶意触发器，恢复模型完整性。这为联邦学习中的数据遗忘提供了实用解决方案。