English(EN) Federated Concept-Based Models: Interpretable models with distributed supervision

新的F-CMs方法实现了具有分布式概念监督的可解释联邦学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员推出了一种名为联邦概念模型（F-CMs）的新颖方法，旨在增强联邦学习框架内深度学习模型的可解释性。该方法允许在拥有分布式概念标注的多个机构之间训练模型，解决了概念标注成本高昂且稀缺的挑战。F-CMs 能够在保护隐私的同时聚合概念级信息，并使模型架构适应不断变化的概念监督，保持与完全监督设置相当的准确性，并优于非自适应联邦基线。 AI

影响在分布式环境中实现更具可解释性的AI模型，有可能提高敏感领域的信任度和采用率。

排序理由该集群描述了arXiv论文中提出的一种新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Signal score: 26 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了arXiv论文中提出的一种新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Dario Fenoglio, Arianna Casanova, Francesco De Santis, Gabriele Dominici, Johannes Schneider, Pietro Barbiero, Giovanni De Felice, Marc Langheinrich, Martin Gjoreski · 2026-11-05 05:00

联邦概念模型：具有分布式监督的可解释模型

arXiv:2602.04093v3 Announce Type: replace Abstract: Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept annotations are costly and rarely available at scale within a single data source. F…

本文如何被排名

报道来源 [1]

联邦概念模型：具有分布式监督的可解释模型

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