面向高效类别遗忘的正交软剪枝方法
Orthogonal Soft Pruning for Efficient Class Unlearning
AI总结：
针对联邦学习中数据遗忘的性能权衡与非IID下特征纠缠问题，提出FedOrtho框架，结合正交卷积核与单次软剪枝实现多粒度高效遗忘，取得SOTA效果并大幅降低成本。
摘要：
联邦学习中高效可控的数据遗忘仍面临挑战，核心难点在于遗忘性能与保留性能之间的权衡，尤其在非独立同分布（non-IID）设置下，深度特征纠缠会进一步加剧这一困境。为解决该问题，我们提出FedOrtho联邦遗忘框架，将正交化深度卷积核与激活驱动的可控单次软剪枝（OSP）机制相结合。FedOrtho通过强制核正交性与局部-全局对齐来解耦特征表示，缓解客户端漂移问题；这种结构独立性使得我们可以在保留已有知识的同时，对遗忘相关的卷积核进行精准的单次剪枝。FedOrtho在CIFAR-10、CIFAR-100、TinyImageNet数据集上搭配ResNet、VGG框架取得了SOTA性能，验证其支持类别级、客户端级、样本级遗忘，遗忘质量超过98%。该方法在联邦设置下将计算与通信成本降低2-3个数量级，在集中式场景下可实现亚秒级擦除，同时保持97%以上的保留准确率，并缓解成员推理风险。
英文摘要：
Efficient and controllable data unlearning in federated learning remains challenging, due to the trade-off between forgetting and retention performance. Especially under non-independent and identically distributed (non-IID) settings, where deep feature entanglement exacerbates this dilemma. To address this challenge, we propose FedOrtho, a federated unlearning framework that combines orthogonalized deep convolutional kernels with an activation-driven controllable one-shot soft pruning (OSP) mechanism. FedOrtho enforces kernel orthogonality and local-global alignment to decouple feature representations and mitigate client drift. This structural independence enables precise one-shot pruning of forgetting-related kernels while preserving retained knowledge. FedOrtho achieves SOTA performance on CIFAR-10, CIFAR100 and TinyImageNet with ResNet and VGG frameworks, verifying that FedOrtho supports class-, client-, and sample-level unlearning with over 98% forgetting quality. It reduces computational and communication costs by 2-3 orders of magnitude in federated settings and achieves subsecond-level erasure in centralized scenarios while maintaining over 97% retention accuracy and mitigating membership inference risks.
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。
