Jetson 임베디드 보드에서 도커 컨테이너를 사용한 연합 학습 기반 이미지 분류

초록

In this research, we have studied a federated learning-based image classification algorithm. In this regard, we have used Resnet18 classifier as a backbone deep learning algorithm for image classification. The training process of this algorithm involved federated learning, such as training is performed at multiple clients (Jetson Nano and TX2) and is averaged at the server (Jetson Xavier). We have exploited the Nvidia Docker container image to deploy our algorithms for the training process. For our experiments, we have used only two clients and one server during the training process of the Resnet18 image classifier. The extension of the CIFAR10 (50K to 500K samples) dataset has been used for training, known as EC10, which contained 1000 subsets for IID client distribution. We have validated the accuracy for both using the federated learning process and traditional training at several strategies and have presented the results correspondingly