A Parameter Aggregation Strategy on Personalized Federated Learning
We investigate the parameter aggregation weights of federated learning (FL), simulate a variety of data access scenarios for experiments, and propose a model parameter weight self-learning strategy for horizontal FL. For application use of this study, a personalized FL network structure model based on edge computing is designed.
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Edge-computingFederated LearningPersonalized Federated LearningSelf-LearningMethods 이 논문이 사용한 방법론
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