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English(EN) From Classifier Heads to Encoder Prototypes: Personalized Federated Learning with Head-Derived Clustering

新个性化联邦学习方法HDC-FL表现强劲

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的个性化联邦学习方法HDC-FL，该方法利用客户端特定的分类器头来指导表示共享。该方法将客户端描述符分组并为每个组创建编码器原型，然后将这些原型返回给客户端。在各种CIFAR-10和CIFAR-100配置下，HDC-FL在与现有的个性化和聚类联邦学习基线相比时，表现出有竞争力的性能，尤其在宏平均最差客户端和底部20%准确率方面表现出色。 AI

影响 该方法可以通过更好地管理客户端表示共享来提高联邦学习系统的效率和有效性。

排序理由 该集群包含一篇详细介绍个性化联邦学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新个性化联邦学习方法HDC-FL表现强劲

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该集群包含一篇详细介绍个性化联邦学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Amirardalan Dehghanpour, Arian Noori, Ehsan Tanghatari, Naimeh Omidvar, Hamid R. Rabiee ·

    从分类器头到编码器原型：基于头派生聚类的个性化联邦学习

    arXiv:2610.30075v1 Announce Type: new Abstract: Personalized federated learning must decide which clients should share representations. We introduce HDC-FL (Head-Derived Clustering for Personalized Federated Learning), which reads that decision off a signal shared-encoder trainin…

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