Improved Federated Average Algorithm Based on Tomographic Analysis

Abstract

In the federated average algorithm,the weight update is used to update the global model.The algorithm only considers the size of the data volume of each client when the weight is updated,and does not consider the impact of data quality on the mo-del.An improvement based on analytic hierarchy is proposed.The federated averaging algorithm is the first to process multi-source data from the perspective of data quality.First,the entropy method is used to calculate the importance of each attribute in the data,and it is used as the value of the criterion layer in the level analysis to calculate the data of each client quality.Then,combined with the amount of data on the client,the weight update method is recalculated in the global model.The simulation results show that for small and medium data sets,the model trained with support vector machines has the highest accuracy,rea-ching 85.7152%.For large data sets,the model trained with random forest has the highest accuracy,reaching 91.9321%.Compared with the traditional federal average method,the accuracy rate is increased by 3.5% on small and medium data sets and 1.3% on large data sets,which can improve the accuracy of the model while improving the security of the data and model.

Keywords

• federated average(fedavg)|entropy weight method|tomographic analysis|weight update