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English(EN) Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment

Flower Hub平台增强联邦学习基准测试可复现性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了Flower Hub，一个旨在提高联邦学习基准测试可复现性和可比性的新平台。该平台允许将基准测试打包成可执行的、版本化的应用程序，并附带标准化的元数据和明确的评估工作流程。Flower Hub支持多种应用，包括医学影像、金融数据、法律文本和音频分析，并且可以在模拟和真实部署环境中运行这些基准测试，无需修改代码。该平台还通过包含运行时和通信指标以及模型性能来促进系统感知报告。 AI

影响 标准化联邦学习评估，有望加速去中心化AI模型的研究和部署。

排序理由 该集群描述了一个在arXiv论文中详细介绍的联邦学习基准测试新平台。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Flower Hub平台增强联邦学习基准测试可复现性

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该集群描述了一个在arXiv论文中详细介绍的联邦学习基准测试新平台。[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) · Yan Gao, Mohammad Naseri, Javier Fernandez-Marques, Dimitris Stripelis, Lorenzo Sani, Davide Eynard, Fan Zhang, Hong Jia, Ting Dang, D. B. Emerson, Fatemeh Tavakoli, Ole Werger, Lars Wulfert, Petros Demetrakopoulos, Sofia Tsekeridou, InSeo Song, KangYoon… ·

    Flower Hub：用于模拟和部署中联邦学习的可复现基准测试平台

    arXiv:2608.25114v1 Announce Type: new Abstract: Federated learning (FL) has emerged as a key approach for training models across decentralized data, yet benchmarking in FL remains difficult to reproduce, compare, and extend. Existing evaluations are often tied to custom infrastru…

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