TY - JOUR
T1 - DeepSwarm
T2 - towards swarm deep learning with bi-directional optimization of data acquisition and processing
AU - Liu, Sicong
AU - Guo, Bin
AU - Wang, Ziqi
AU - Wang, Lehao
AU - Zhou, Zimu
AU - Li, Xiaochen
AU - Yu, Zhiwen
N1 - Publisher Copyright:
© Higher Education Press 2025.
PY - 2025/3
Y1 - 2025/3
N2 - Inspired by the collective intelligence observed in natural swarms, where individual proactive actions contribute to superior global performance, we advocate for a shift towards Swarm DL. By harnessing the potential of physically adjacent mobile devices in IoT scenarios, we present DeepSwarm, a closed-loop system framework architecture. DeepSwarm facilitates bidirectional optimization between data acquisition and processing, aiming to push the performance boundaries of on-device DL Specifically, DeepSwarm addresses the requirements of proactive Swarm DL by decomposing them into layers: self-organized swarm data acquisition and self-adaptive, self-evolutionary swarm data processing.
AB - Inspired by the collective intelligence observed in natural swarms, where individual proactive actions contribute to superior global performance, we advocate for a shift towards Swarm DL. By harnessing the potential of physically adjacent mobile devices in IoT scenarios, we present DeepSwarm, a closed-loop system framework architecture. DeepSwarm facilitates bidirectional optimization between data acquisition and processing, aiming to push the performance boundaries of on-device DL Specifically, DeepSwarm addresses the requirements of proactive Swarm DL by decomposing them into layers: self-organized swarm data acquisition and self-adaptive, self-evolutionary swarm data processing.
UR - https://www.scopus.com/pages/publications/85209698411
U2 - 10.1007/s11704-024-40465-z
DO - 10.1007/s11704-024-40465-z
M3 - 快报
AN - SCOPUS:85209698411
SN - 2095-2228
VL - 19
JO - Frontiers of Computer Science
JF - Frontiers of Computer Science
IS - 3
M1 - 193501
ER -