English(EN) EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning

新框架通过进化客户端选择增强联邦学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-09 04:00

研究人员开发了一个名为 EvoCSFL 的新框架，以提高联邦学习的效率和鲁棒性。该方法使用由代理模型指导的进化算法来选择客户端，以优化模型性能、通信延迟和能耗。在多个数据集上的实验表明，与现有方法相比，EvoCSFL 实现了更快的收敛速度、更低的能耗和更好的鲁棒性。 AI

影响这一新框架可能导致更高效、更鲁棒的分布式人工智能模型训练，尤其是在客户端多样化且可能不可靠的环境中。

排序理由这是一篇详细介绍联邦学习新算法框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Lin Qiang, Sun Xiaoyan, Hu Yao, Fang Wei · 2026-06-09 04:00

EvoCSFL：用于高效鲁棒联邦学习的代理辅助进化客户端选择

arXiv:2606.07702v1 Announce Type: cross Abstract: The heterogeneity of client data and systems makes it difficult to achieve satisfactory convergence speed and robustness in federated learning with random client selection. To address this issue, this paper proposes a surrogate-as…