FlexFL: Heterogeneous federated learning via APoZ-guided flexible pruning in uncertain scenarios

• Main Authors: CHEN, Zekai, JIA, Chentao, HU, Ming, XIE, Xiaofei, LI, Anran, CHEN, Mingsong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: AIoT; APoZ; Heterogeneous federated learning; Model pruning; Uncertain scenario; Artificial Intelligence and Robotics; Computer Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/9817; https://ink.library.smu.edu.sg/context/sis_research/article/10817/viewcontent/2407.12729v1.pdf
• Institution: Singapore Management University

Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable privacy-aware collaborative learning among AIoT devices. However, due to the inherent data and device heterogeneity iss...