Autoencoder-Enhanced Federated Learning with Reduced Overhead and Lower Latency

Chi Kai Hsieh*, Feng Tsun Chien*, Min Kuan Chang

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
發行者Institute of Electrical and Electronics Engineers Inc.
頁面2118-2123
頁數6
ISBN(電子)9798350300673
DOIs
出版狀態Published - 2023
事件2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, Taiwan
持續時間: 31 10月 20233 11月 2023

出版系列

名字2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Conference

Conference2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
國家/地區Taiwan
城市Taipei
期間31/10/233/11/23

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