Year: 2023
Type: article
Abstract: Federated learning allows you to train machine learning models without sharing your local data. Due to the No-iid problem, this paper is based on the Moon algorithm, which can have excellent performance in datasets of images with models that use deep learning and outperforms FedAvg, FedProx, and other algorithms, with the goal to decrease communication costs while enhancing efficiency more effectively. This study optimizes its gradient descent technique based on Moon's algorithm by utilizing Adaptive Gradient (AdaGrad) optimizer and combining with knowledge distillation to improve Moon's algorithm in order to better reduce communication costs and improve efficiency. That is, it reduces the loss and improves the accuracy faster and better in local training. In this paper, we experimentally show that the optimized moon can better solve the communication cost and improve the accuracy rate.
Authors: Enming Chu, Dengbo Li, Yangfan Tong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4387896101