English(EN) Beyond Conventional Federated Learning via High-Order Regularization

新的HiFedProx方法增强了联邦学习的正则化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种名为HiFedProx的新型联邦学习正则化技术，该技术改进了现有的FedProx方法。HiFedProx用p索引的幂类型正则化器替换了二次惩罚，从而能够更好地控制客户端参数的位移。在FEMNIST子集上的实验表明，HiFedProx在压力条件下取得了显著的收益，并且在p值为5到7之间观察到了最佳性能。 AI

影响引入了一种新颖的正则化技术，可以提高分布式环境中联邦学习模型的鲁棒性和性能。

排序理由这是一篇详细介绍联邦学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Alireza Kabgani, Masoud Ahookhosh · 2026-09-10 04:00

超越传统联邦学习的高阶正则化方法

arXiv:2609.09904v1 Announce Type: cross Abstract: Federated clients that perform several local optimization steps can return parameter displacements with widely different magnitudes. The quadratic regularization of FedProx grows linearly with displacement and therefore offers lim…

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超越传统联邦学习的高阶正则化方法

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