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English(EN) TRIP-Shapley: Propagation-Aware Contribution Attribution Framework for Peer-to-Peer Federated Learning

TRIP-Shapley框架改进P2P联邦学习归因

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了TRIP-Shapley，一个用于点对点联邦学习（P2PFL）中贡献归因的新框架。该方法解决了DICE等先前方法固有的通信开销和错误报告攻击漏洞。TRIP-Shapley为每一轮独立计算贡献分数，仅传播标量值以保持实际通信成本，同时仍能考虑整个训练过程中的影响。实验表明，与DICE相比，通信节省显著，并且能有效防御错误报告攻击。 AI

影响 该框架为联邦学习中的贡献归因提供了一种更有效、更安全的方法，有望改善去中心化人工智能训练中的协作和信任。

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 →

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

TRIP-Shapley框架改进P2P联邦学习归因

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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]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Honoka Anada, Tatsuya Kaneko, Yafei Ou, Shinya Takamaeda-Yamazaki ·

    TRIP-Shapley：面向点对点联邦学习的传播感知贡献归因框架

    arXiv:2505.23246v3 Announce Type: replace Abstract: Contribution attribution is a fundamental problem in federated learning (FL), yet it remains challenging in peer-to-peer FL (P2PFL), where participant influence propagates through repeated model exchanges. Recent work, DICE, add…

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