A Distributed Non-IID Dataset Generation Method for Federated Learning

• 1、Computer Science, Beijing University of Post and Telecommunications, Beijing, 100879

Abstract：Federated learning is a kind of distributed machine learning. In federated learning, distributed edge devices (such as mobile phones) can collaboratively learn shared prediction models, while keeping all training data on the device, not only can make full use of the data trained models distributed on multiple nodes, but also data privacy is also protected. However, cross-device data is likely to be Non-IID (non-independent and identically distributed), which may make the performance of the model trained through federated learning unstable. Currently, the distributed Non-IID data set that can support federated learning research is still vacant. Therefore, this paper proposes a dataset generation method suitable for the federated learning training framework, and also provides two federated datasets for image classification tasks, including the artificially generated V-MNIST dataset and the naturally collected Third-Eye dataset. At the same time, the measurement parameters of the data set are formulated, including the number of nodes, the amount of data, the Euclidean distance of the node data distribution, etc., and experiments on these data sets are carried out on the federated learning framework to verify their application effects in federated learning.

Keywords： Machine Learning Federated Learning Non-IID Dataset Image Classification Task Deep Learning