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English(EN) EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure

EASE框架通过解决纠缠问题实现联邦多模态遗忘

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了EASE，一种用于联邦多模态遗忘的新框架，该框架解决了不同数据模态和客户端更新之间纠缠知识的挑战。该方法识别导致遗忘信息持续存在的三个关键“锚点”，并提出切断这些连接的技术。EASE利用双边位移处理跨模态通道，并使用余弦-正弦分解来分离遗忘专属更新方向，旨在有效删除特定数据同时保留模型的通用能力。 AI

影响 改进了多模态联邦系统中保护隐私的遗忘功能，可能实现更可靠的数据删除。

排序理由 学术论文，介绍了一种新颖的联邦遗忘方法。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

EASE框架通过解决纠缠问题实现联邦多模态遗忘

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学术论文，介绍了一种新颖的联邦遗忘方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zihao Ding, Beining Wu, Jun Huang ·

    EASE：通过纠缠感知锚点闭合实现联邦多模态遗忘

    arXiv:2605.00733v1 Announce Type: cross Abstract: Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint embedding training entangles forgotten knowledge across both modalities and cli…

  2. arXiv cs.AI TIER_1 English(EN) · Jun Huang ·

    EASE：通过纠缠感知锚点闭合实现联邦多模态遗忘

    Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint embedding training entangles forgotten knowledge across both modalities and client gradient subspaces, hindering federated unlear…

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