Towards Integrated Fine-tuning and Inference when Generative AI meets
  Edge Intelligence
Ning Chen Zhipeng Cheng Xuwei Fan Xiaoyu Xia Lianfen Huang
The high-performance generative artificial intelligence (GAI) represents the
latest evolution of computational intelligence, while the blessing of future 6G
networks also makes edge intelligence (EI) full of development potential. The
inevitable encounter between GAI and EI can unleash new opportunities, where
GAI's pre-training based on massive computing resources and large-scale
unlabeled corpora can provide strong foundational knowledge for EI, while EI
can harness fragmented computing resources to aggregate personalized knowledge
for GAI. However, the natural contradictory features pose significant
challenges to direct knowledge sharing. To address this, in this paper, we
propose the GAI-oriented synthetical network (GaisNet), a collaborative
cloud-edge-end intelligence framework that buffers contradiction leveraging
data-free knowledge relay, where the bidirectional knowledge flow enables GAI's
virtuous-cycle model fine-tuning and task inference, achieving mutualism
between GAI and EI with seamless fusion and collaborative evolution.
Experimental results demonstrate the effectiveness of the proposed mechanisms.
Finally, we discuss the future challenges and directions in the interplay
between GAI and EI.