@inproceedings{6b30db591daf45c89eb3c083ce81afec,
title = "Autoencoder-Enhanced Federated Learning with Reduced Overhead and Lower Latency",
abstract = "This paper investigates the application of autoencoder (AE) in supporting the training process of federated learning by reducing communication overhead and latency. We propose a scheduling algorithm to determine when and how to use autoencoder during training. Our simulation shows that federated learning with an autoencoder significantly reduces communication overhead without compromising testing accuracy. Moreover, the testing accuracy curve shows a more consistent increase over training rounds in federated learning with an autoencoder than in federated learning without an autoencoder. Additionally, the latency of federated learning with an autoencoder is lower than that of federated learning without an autoencoder.",
keywords = "Federated learning, autoencoder, scheduling",
author = "Hsieh, {Chi Kai} and Chien, {Feng Tsun} and Chang, {Min Kuan}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 ; Conference date: 31-10-2023 Through 03-11-2023",
year = "2023",
doi = "10.1109/APSIPAASC58517.2023.10317190",
language = "English",
series = "2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2118--2123",
booktitle = "2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023",
address = "美國",
}