English(EN) FedSharp: Byzantine Robust Decentralized Federated Learning Under Noisy Channel

FedSharp框架增强了分布式联邦学习的抗攻击能力

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

研究人员开发了FedSharp，一个旨在增强分布式联邦学习系统在拜占庭攻击和信道噪声下的鲁棒性的新框架。该方法通过结合筛选、跳数感知、排序和近邻平均技术，解决了模型传播过程中噪声累积的问题。实验表明，FedSharp在MNIST和CIFAR-10等数据集上显著提高了性能，在存在大量拜占庭节点和噪声信道的场景下优于现有基准。 AI

影响提高了分布式AI训练的安全性和可靠性，可能促进更鲁棒的协作模型开发。

排序理由该集群包含一篇详细介绍分布式联邦学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Ojaswi Sinha, Amin Farajzadeh, Animesh Yadav, Melike Erol-Kantarci · 2026-11-05 05:00

FedSharp：嘈杂信道下的拜占庭鲁棒去中心化联邦学习

arXiv:2611.05546v1 Announce Type: new Abstract: Decentralized federated learning (DFL) eliminates the central aggregation server but remains vulnerable to data heterogeneity, Byzantine attacks, and channel noise. We show that noise accumulates as models propagate across nodes, be…

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FedSharp：嘈杂信道下的拜占庭鲁棒去中心化联邦学习

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