Federated Data Augmentation Algorithm for Non-independent and Identical Distributed Data

Abstract

In federated learning,the local data distribution of users changes with the location and preferences of users,the data under the non-independent and identical distributed(Non-IID) data may lack data of some label categories,which significantly affects the update rate and the performance of the global model in federated aggregation.To solve this problem,a federated data augmentation based on conditional generative adversarial network(FDA-cGAN) algorithm is proposed,which can amplify data from participants with skewed data without compromising user privacy,and greatly improve the performance of the algorithm with Non-IID data.Experimental results show that,compared with the current mainstream federated average algorithm,under the Non-IID data setting,the prediction accuracy of MNIST and CIFAR-10 data sets improves by 1.18% and 14.6%,respectively,which demonstrates the effectiveness and practicability of the proposed algorithm for Non-IID data problems in federated learning.

Keywords

• federated learning|privacy-preserving|generative adversarial network|differential privacy|data augmentation