English(EN) Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach

新的博弈论方法增强了簇状联邦学习的稳定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-30 04:00

研究人员开发了一种新的簇状联邦学习联盟形成方法，旨在实现稳定且经济可行的参与者分组。他们的方法利用了可转移盈余模型和享乐偏好系统，以确保参与者有动力加入并留在其分配的联盟中。该博弈论框架保证了纳什稳定划分的存在，并证明了他们的机制在实证研究中实现了最优福利。 AI

影响为提高分布式机器学习系统的效率和稳定性引入了理论框架。

排序理由详细介绍机器学习问题新理论方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Cengis Hasan · 2026-07-30 04:00

面向聚类联邦学习的稳定且经济可行的联盟形成：一种享乐势博弈方法

arXiv:2607.26788v1 Announce Type: cross Abstract: Clustered federated learning benefits from organizing heterogeneous participants into coalitions that train coalition-specific models, but such clustering is sustainable only if participants prefer their assigned coalition and the…