TY - GEN
T1 - Adaptive Clustering and Step-Size Optimization in Collaborative Distributed Diffusion-Based AIGC
T2 - 15th International Conference on Information and Communication Technology Convergence, ICTC 2024
AU - Xu, Zeliang
AU - Kim, Dong In
AU - Woo, Simon S.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper proposes a novel cloud-edge collaborative distributed diffusion model for AI-generated content (AIGC) such as image generation, which integrates adaptive clustering techniques with dynamic step-size optimization. The proposed model addresses the challenges of heterogeneous edge devices in real-world deployments. Experimental results demonstrate significant improvements in performance and efficiency with a 38.8% reduction in average generation time and a 15.6% increase in image quality (evaluate via CLIP score). The system shows enhanced resource utilization, improving cloud and edge utilization by 16.1 % and 36.6%, respectively. This research contributes to the advancement of collaborative distributed diffusion model, offering a scalable and adaptive framework for efficient AIGC services in dynamic environments along with potential applications extending to other computationally intensive tasks in cloud-edge systems.
AB - This paper proposes a novel cloud-edge collaborative distributed diffusion model for AI-generated content (AIGC) such as image generation, which integrates adaptive clustering techniques with dynamic step-size optimization. The proposed model addresses the challenges of heterogeneous edge devices in real-world deployments. Experimental results demonstrate significant improvements in performance and efficiency with a 38.8% reduction in average generation time and a 15.6% increase in image quality (evaluate via CLIP score). The system shows enhanced resource utilization, improving cloud and edge utilization by 16.1 % and 36.6%, respectively. This research contributes to the advancement of collaborative distributed diffusion model, offering a scalable and adaptive framework for efficient AIGC services in dynamic environments along with potential applications extending to other computationally intensive tasks in cloud-edge systems.
KW - adaptive clustering
KW - AI-generated content (AIGC)
KW - collaborative distributed computing
KW - heterogeneous networks
KW - resource utilization
KW - step-size optimization
UR - https://www.scopus.com/pages/publications/85217624941
U2 - 10.1109/ICTC62082.2024.10826855
DO - 10.1109/ICTC62082.2024.10826855
M3 - Conference contribution
AN - SCOPUS:85217624941
T3 - International Conference on ICT Convergence
SP - 307
EP - 312
BT - ICTC 2024 - 15th International Conference on ICT Convergence
PB - IEEE Computer Society
Y2 - 16 October 2024 through 18 October 2024
ER -