吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (10): 3337-3345.doi: 10.13229/j.cnki.jdxbgxb.20231369
• 计算机科学与技术 • 上一篇
资源高效的聚类协同联邦学习客户端选择方法
李强(
),张凌羽,孟祥宇()
- 吉林大学 计算机科学与技术学院，长春 130012
Resource-efficient clustering collaborative federated learning client selection method
Qiang LI(),Ling-yu ZHANG,Xiang-yu MENG()
- College of Computer Science and Technology，Jilin University，Changchun 130012，China
摘要：
针对联邦学习中各客户端存在资源异构性和数据异构性的关键问题，提出了一种资源高效的聚类协同联邦学习客户端选择方法。首先，根据各客户端的计算能力将其分组，在每轮训练中，以每组客户端的平均准确率作为间接度量选择同组客户端；其次，在每组内根据各客户端的模型相似性对客户端进行聚类，选择每组内不同聚类的客户端；最后在真实数据集上评估本文方法的性能。实验结果表明：该方法可以减少全局训练时间，获得更快速、更平滑的收敛，实现训练效率和全局模型准确率之间的良好平衡。
中图分类号:
- TP301
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