English(EN) Efficient Exact Unlearning for Federated Zeroth-Order Optimization

新方法实现联邦AI训练的高效精确遗忘

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了面向联邦零阶优化（FedZOO）的高效精确遗忘新方法。这些技术，FedZOUL-S和FedZOUL-NS，利用全变分稳定性来重用计算并降低删除特定训练数据影响的成本。实验表明，与重新优化相比，这些方法在模型效用方面具有竞争力，遗忘时间显著减少，并且与一阶基线相比通信开销大大降低。 AI

影响通过高效处理数据删除请求，实现更强大、更注重隐私的协作式AI训练。

排序理由详细介绍新算法及其理论保证的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Weijun Yin, Yanqing Yao, Falko Dressler, Di Wang, Youming Tao · 2026-10-27 04:00

面向联邦零阶优化的高效精确遗忘

arXiv:2610.35211v1 Announce Type: cross Abstract: Federated zeroth-order optimization (FedZOO) reduces the memory and communication costs of collaborative training for edge intelligence by avoiding backpropagation while communicating only random seeds and scalar coefficients. How…

本文如何被排名

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面向联邦零阶优化的高效精确遗忘

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