English(EN) Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning

Federated learning model explores client self-interest and equilibrium transitions

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了一个潜在博弈框架来模拟客户出于自身利益行事的联邦学习场景。该模型分析了客户在服务器奖励影响下的理性训练选择如何导致纳什均衡。研究表明，这些均衡会随着关键奖励因子发生非线性转变，可能导致客户在低努力和高努力水平之间切换。该论文还验证了该关键因子在联邦学习训练中的有效性。 AI

排序理由这是一篇发表在 arXiv 上的研究论文，详细介绍了一种新的联邦学习模型。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Kang Liu, Ziqi Wang, Enrique Zuazua · 2026-06-02 04:00

Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning

arXiv:2411.11793v2 Announce Type: replace Abstract: In federated learning (FL), a central server typically allocates training efforts to clients. However, from a market-oriented perspective, clients may independently choose their training efforts based on rational self-interest. …