Kavli Affiliate: Cheng Peng
| First 5 Authors: Yujie Zhou, Yujie Zhou, , ,
| Summary:
Semantic communication (SemCom) shifts the focus from data transmission to
meaning delivery, enabling efficient and intelligent communication.
Existing AI-based coding schemes for multi-modal multi-task SemCom often
require transmitters with full-modal data to participate in all receivers’
tasks, which leads to redundant transmissions and conflicts with the physical
limits of channel capacity and computational capability.
In this paper, we propose PoM$^2$-DIB, a novel framework that extends the
distributed information bottleneck (DIB) theory to address this problem.
Unlike the typical DIB, this framework introduces modality selection as an
additional key design variable, enabling a more flexible tradeoff between
communication rate and inference quality.
This extension selects only the most relevant modalities for task
participation, adhering to the physical constraints, while following efficient
DIB-based coding.
To optimize selection and coding end-to-end, we relax modality selection into
a probabilistic form, allowing the use of score function estimation with common
randomness to enable optimizable coordinated decisions across distributed
devices.
Experimental results on public datasets verify that PoM$^2$-DIB achieves high
inference quality compared to full-participation baselines in various tasks
under physical limits.
| Search Query: ArXiv Query: search_query=au:”Cheng Peng”&id_list=&start=0&max_results=3