English(EN) Efficient Knowledge Transfer in Federated Bayesian Optimization through Neural Network Surrogates

新的GLOBAFED框架通过神经网络增强了联邦贝叶斯优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-14 05:00

研究人员推出了一种名为GLOBAFED的新框架，旨在通过实现具有不同目标代理之间的高效知识迁移来改进联邦贝叶斯优化（FedBO）。该方法利用一个具有共享特征提取主干和代理特定头的概率神经网络，并采用变分贝叶斯方法进行不确定性估计。GLOBAFED通过联邦学习促进了对通用潜在特征表示的协作学习，使代理能够在保持隐私的同时适应其独特目标。实证结果表明，GLOBAFED在收敛速度和最终性能方面优于现有的FedBO方法。 AI

影响该框架可以提高分布式AI模型训练和优化任务的效率和性能。

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arXiv cs.AI TIER_1 English(EN) · Alexander Gr\"afe, Max van Gemmeren, Paul Brunzema, Sebastian Trimpe · 2026-11-14 05:00

通过神经网络代理在联邦贝叶斯优化中实现高效知识迁移

arXiv:2611.15707v1 Announce Type: cross Abstract: Federated Bayesian Optimization (FedBO) has emerged as a powerful paradigm for enhancing Bayesian Optimization (BO) in distributed, privacy-sensitive settings, enabling agents to accelerate the optimization of their local objectiv…

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通过神经网络代理在联邦贝叶斯优化中实现高效知识迁移

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