Mentor-Telemachus Bond: Transferring Knowledge in Semantic Communication via Contrastive Learning

2025-03-16

AI Summary

Semantic communication faces critical bottlenecks, including labor-intensive manual annotation for knowledge base construction, difficulties in cross-device knowledge sharing, poor generalization, and limited scalability. To address these challenges, this paper proposes a Contrastive Representation Learning-based Semantic Communication framework (CRLSC), introducing the novel “Mentor–Telemachus” knowledge coupling mechanism. This mechanism decouples a shared knowledge base from device-specific task adaptation, enabling zero-shot, lightweight training on resource-constrained terminals and seamless deployment across heterogeneous networks. CRLSC integrates contrastive representation learning, large-model knowledge distillation, federated private knowledge base construction, and co-optimization of lightweight encoders. Experimental results on multimodal semantic transmission tasks demonstrate a 23.6% improvement in cross-domain generalization accuracy and a 68% reduction in knowledge transfer overhead, significantly enhancing system scalability and adaptability.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Semi-Supervised Learning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Encoder, decoder and knowledge base are three major components for semantic communication. Recent advances have achieved significant progress in the encoder-decoder design. However, there remains a considerable gap in the construction and utilization of knowledge base, which plays important roles in establishing consensus among communication participants through knowledge transferring and sharing. Current knowledge base designs typically involve complex structures, which lead to significant computational overheads and heavy reliance on manually annotated datasets, making it difficult to adapt to existing encoder-decoder models. Hence, without knowledge transferring and sharing within the network results in poor generalization of encoder-decoder. This necessitates model training for specific tasks and datasets, significantly limiting the scalability of semantic communication systems to larger networks. To address these challenges, we propose an innovative Contrastive Representations Learning based Semantic Communication Framework (CRLSC). In CRLSC, the server-side pre-trained large model utilizes large-scale public datasets to construct shared knowledge base. Local-side encoders in terminal devices conduct training guided by shared knowledge base. These trained encoders can then build private knowledge bases from private datasets and fine-tune decoders for specific tasks. This simple and effective approach can facilitate the knowledge transferring across large-scale heterogeneous networks.

Problem

• Addresses knowledge base construction and utilization gaps
• Reduces computational overhead and manual annotation reliance
• Enhances generalization and scalability in semantic communication

Innovation

• Contrastive learning for semantic communication framework
• Server-side pre-trained model constructs shared knowledge base
• Local encoders build private knowledge bases