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English(EN) Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners

新的机制激励联邦学习中的数据贡献

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文介绍了三种旨在激励联邦学习场景中数据贡献的机制。提出的方法 UPBReD、2P-UPBReD 和 T-2P-UPBReD，旨在确保战略数据所有者为共享模型的训练做出充分贡献。这些机制不仅鼓励参与，还同时学习模型参数，其中 T-2P-UPBReD 专门设计用于引发真实的贡献报告和近似梯度真实性，同时保持预算平衡。 AI

影响 引入了联邦学习的新型激励机制，有望提高数据贡献和模型训练效率。

排序理由 介绍联邦学习新机制的学术论文。[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 →

新的机制激励联邦学习中的数据贡献

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介绍联邦学习新机制的学术论文。[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) · Drashthi Doshi, Avishek Ghosh, Swaprava Nath ·

    激励贡献并学习参数：具有战略性数据所有者的联邦学习

    arXiv:2505.12010v5 Announce Type: replace-cross Abstract: In federated learning (FL), a center trains a shared model from updates that clients compute on their private data. Collecting and training on data is costly, so a strategic client may contribute little data and free-ride …

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