CaBaFL: Asynchronous federated learning via hierarchical cache and feature balance

• Main Authors: XIA, Zeke, HU, Ming, YAN, Dengke, XIE, Xiaofei, LI, Tianlin, LI, Anran, ZHOU, Junlong, CHEN, Mingsong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Artificial Intelligence of Things AIoT); asynchronous federated learning; data/device heterogeneity; feature balance; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/9563
• Institution: Singapore Management University

Federated learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency and inference capability of FL is seriously limited due to the presence of stragglers and data imbalance across massi...