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English(EN) Communication-Efficient Secure Aggregation in Decentralized Learning

CESAR协议赋能私密、高效的去中心化学习

一种名为CESAR的新协议已被开发出来,以解决去中心化学习中的通信和隐私挑战。CESAR将安全聚合与稀疏化相结合,允许参与者在没有中央服务器的情况下协同训练模型,同时保持隐私。该协议支持节点掉线和强大的隐私保护,而无需中央聚合器。实证评估表明,CESAR可以显著减少数据交换,并达到非私有基线的准确性。 AI

影响 实现更私密、更高效的协同模型训练,可能降低去中心化AI系统中的通信开销。

排序理由 该集群包含一篇详细介绍去中心化学习新协议的研究论文。[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 →

CESAR协议赋能私密、高效的去中心化学习

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该集群包含一篇详细介绍去中心化学习新协议的研究论文。[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) · Sayan Biswas, Anne-Marie Kermarrec, Rafael Pires, Rishi Sharma, Milos Vujasinovic ·

    去中心化学习中的通信高效安全聚合

    arXiv:2405.07708v3 Announce Type: replace Abstract: Decentralized learning (DL) enables participants to collaboratively train models without a central server, yet it faces significant scalability challenges that demand sparsification to reduce the prohibitive communication costs …