Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion

作     者：Ke Li Xiaofeng Wang Hu Wang 

作者机构：College of Computer Science and EngineeringNorth Minzu UniversityYinchuan750021China The Key Laboratory of Images&Graphics Intelligent Processing of State Ethnic Affairs CommissionNorth Minzu UniversityYinchuan750021China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第81卷第10期

页      面：1391-1407页

核心收录：

学科分类：0839[工学-网络空间安全] 08[工学] 

基　　金：the National Natural Science Foundation of China(Grant No.62062001) Ningxia Youth Top Talent Project(2021) 

主　　题：Federated learning data heterogeneity feature alignment decision fusion hierarchical optimization 

摘      要：In the realm of data privacy protection,federated learning aims to collaboratively train a global ***,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global *** shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized ***,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model *** tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local ***,FedFCD employs a hierarchical optimization strategy to flexibly optimize model *** experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of *** instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning ***,extended experiments confirm the robustness of FedFCD across various hyperparameter values.