English(EN) FEAST: Federated Shared-Space Training for Resource-Heterogeneous Clients

FEAST框架增强了异构资源客户端的联邦学习能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-11 04:00

研究人员推出了一种新颖的联邦学习框架FEAST，旨在适应计算资源各异的客户端。FEAST训练一个单一的弹性模型（或称“超网”），通过同时学习多个子网络来适应不同客户端的预算。这种方法确保了参数能够被更广泛的客户端访问，从而提高了异构环境下的准确性。在CIFAR-100等数据集上的实验结果表明，FEAST的性能显著优于现有的权重共享方法，实现了更高的总体平均准确率，并减少了模型参数的传输量。 AI

影响提高了具有不同计算能力的设备的联邦学习效率，有望改善异构环境下的模型性能。

排序理由这是一篇详细介绍联邦学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Bostan Khan, Masoud Daneshtalab · 2026-08-11 04:00

FEAST：面向资源异构客户端的联邦共享空间训练

arXiv:2608.09250v1 Announce Type: new Abstract: Federated learning (FL) must serve devices with varying computational capabilities. A fixed model cannot suit all devices, while training one model per deployment limit is costly. Federated supernet training instead learns one elast…

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报道来源 [1]

FEAST：面向资源异构客户端的联邦共享空间训练

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