Fusion Method of Reinforced Federated Learning Model Based on Heterogeneous Features
YU Fa
ZHAO Xiaochu
GU Mu
ZHANG Yuanjie
Abstract：In a federated network,the data of each device is Non-IID(Non-Independent and Identically Distributed),and the computing power between devices is also different.These heterogeneous features will cause the quality of the model learned by each node device to be good or bad.The traditional federated learning averages the weights of the training results of each node and makes the federated learning effect unsatisfactory.Aiming at these heterogeneous features in the federated network,this article de-signs experiments to add reinforcement learning to the federated learning.The model fusion process replaces the traditional average fusion method,improves the learning effect of federated learning in a heterogeneous feature environment,and enhances the robust-ness of federated learning.
Keywords：heterogeneous featuresfederated learningreinforcement learningmodel fusiondeep learning
Publication Date：2023-10-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 2305-2308,2329 )