A Parameter Aggregation Strategy on Personalized Federated Learning

Abstract

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.

Tasks

Reproductions