通信学报 ›› 2023, Vol. 44 ›› Issue (5): 169-180.doi: 10.11959/j.issn.1000-436x.2023095
田有亮1,2,3, 吴柿红1,2, 李沓1,2, 王林冬1,2, 周骅4
修回日期:
2023-04-06
出版日期:
2023-05-25
发布日期:
2023-05-01
作者简介:
田有亮(1982- ),男,贵州盘州人,博士,贵州大学教授、博士生导师,主要研究方向为博弈论、密码学与安全协议、大数据隐私保护基金资助:
Youliang TIAN1,2,3, Shihong WU1,2, Ta LI1,2, Lindong WANG1,2, Hua ZHOU4
Revised:
2023-04-06
Online:
2023-05-25
Published:
2023-05-01
Supported by:
摘要:
针对联邦学习的训练过程迭代次数多、训练时间长、效率低等问题,提出一种基于激励机制的联邦学习优化算法。首先,设计与时间和模型损失相关的信誉值,基于该信誉值,设计激励机制激励拥有高质量数据的客户端加入训练。其次,基于拍卖理论设计拍卖机制,客户端通过向雾节点拍卖本地训练任务,委托高性能雾节点训练本地数据从而提升本地训练效率,解决客户端间的性能不均衡问题。最后,设计全局梯度聚合策略,增加高精度局部梯度在全局梯度中的权重,剔除恶意客户端,从而减少模型训练次数。
中图分类号:
田有亮, 吴柿红, 李沓, 王林冬, 周骅. 基于激励机制的联邦学习优化算法[J]. 通信学报, 2023, 44(5): 169-180.
Youliang TIAN, Shihong WU, Ta LI, Lindong WANG, Hua ZHOU. Federated learning optimization algorithm based on incentive mechanism[J]. Journal on Communications, 2023, 44(5): 169-180.
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