Embedded Implementation and Evaluation of Deep Neural Network of Federated Learning

Abstract

Compared with traditional distributed machine learning, federated learning (or joint learning) enables multiple computing nodes to cooperate and train a shared machine learning model without transmitting original data. At present, the research work of federated learning mainly focuses on the theoretical method, and the system implementation is less, and only for the text data or simple image such as medical institution information sharing, handwriting font recognition and other simple neural network applications. Aiming at more complex deep neural networks, this project implements a multi-node federated learning system on embedded device, and evaluates its key performance indicators such as training accuracy, delay and loss. The research method mainly uses embedded computer both as client and server, adjusts and groups the Visdrone datasets as training samples, and then trains the model on the client based on YOLOv4 algorithm, realizes the encrypted transmission of information through TCP protocol, and achieves the aggregation update of the model on the server with FedAvg algorithm.