Distillation-Enabled Knowledge Alignment for Generative Semantic Communications in AIGC Provisioning Tasks

2025-06-24

AI Summary

To address the inefficiency of AI-generated content (AIGC) transmission in generative semantic communication (GSC), caused by misalignment between cloud-based generative AI and edge/user-side knowledge, as well as mismatch between transmitted semantic representations and dynamic channel conditions, this paper proposes DeKA-g. The method jointly aligns generative and channel knowledge while enabling collaborative compression via meta-word-assisted knowledge distillation and variable-bitrate packet-level SNR adaptation. It integrates low-rank matrix distillation, meta-word optimization, generative semantic encoding, and dynamic communication parameter matching. Experimental results demonstrate that DeKA-g improves semantic consistency between edge-generated images and cloud semantics by 44%, enhances compression-rate adaptation efficiency by 116%, and boosts transmission performance under low-SNR conditions by 28%.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Due to the surging amount of AI-generated content (AIGC), its provisioning to edges and mobile users from the cloud incurs substantial traffic on networks. Generative semantic communication (GSC) offers a promising solution by transmitting highly compact information, i.e., prompt text and latent representations, instead of high-dimensional AIGC data. However, GSC relies on the alignment between the knowledge in the cloud generative AI (GAI) and that possessed by the edges and users, and between the knowledge for wireless transmission and that of actual channels, which remains challenging. In this paper, we propose DeKA-g, a distillation-enabled knowledge alignment algorithm for GSC systems. The core idea is to distill the generation knowledge from the cloud-GAI into low-rank matrices, which can be incorporated by the edge and used to adapt the transmission knowledge to diverse wireless channel conditions. DeKA-g comprises two novel methods: metaword-aided knowledge distillation (MAKD) and variable-rate grouped SNR adaptation (VGSA). For MAKD, an optimized metaword is employed to enhance the efficiency of knowledge distillation, while VGSA enables efficient adaptation to diverse compression rates and SNR ranges. From simulation results, DeKA-g improves the alignment between the edge-generated images and the cloud-generated ones by 44%. Moreover, it adapts to compression rates with 116% higher efficiency than the baseline and enhances the performance in low-SNR conditions by 28%.

Problem

• Aligning cloud and edge knowledge for efficient AIGC transmission
• Adapting transmission knowledge to diverse wireless channel conditions
• Enhancing efficiency of knowledge distillation and SNR adaptation

Innovation

• Distills cloud-GAI knowledge into low-rank matrices
• Employs metaword-aided knowledge distillation (MAKD)
• Adapts transmission via variable-rate grouped SNR (VGSA)