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Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computing by Zeng, Liekang; Ye, Shengyuan; Chen, Xu; Yang, Yang is a scholarly article available to read on EtoBox.

What is Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computing about?

Big Artificial Intelligence (AI) models have emerged as a crucial element in various intelligent applications at the edge, such as voice assistants in smart homes and autonomous robotics in smart factories. Training big AI models, e.g., for personalized fine-tuning and continual model refinement, poses significant challenges to edge devices due to the inherent conflict between limited computing resources and intensive workload associated with training. Despite the constraints of on-device training, traditional approaches usually resort to aggregating training data and sending it to a remote cloud for centralized training. Nevertheless, this approach is neither sustainable, which strains long-range backhaul transmission and energy-consuming datacenters, nor safely private, which shares users' raw data with remote infrastructures. To address these challenges, we alternatively observe that prevalent edge environments usually contain a diverse collection of trusted edge devices with untapped idle resources, which can be leveraged for edge training acceleration. Motivated by this, in this article, we propose collaborative edge training, a novel training mechanism that orchestrates a gro

Author

Zeng, Liekang; Ye, Shengyuan; Chen, Xu; Yang, Yang

Published

2024

Language

EN