Distillation-Enabled Knowledge Alignment Protocol for Semantic Communication in AI Agent Networks

2025-05-07

IEEE Communications Letters

AI Summary

To address semantic alignment challenges among heterogeneous AI agents in agent networks—arising from inconsistent expert knowledge—this paper proposes the Distillation-driven Knowledge Alignment Protocol (DeKAP). DeKAP introduces a novel parameter-efficient representation of expert knowledge via low-rank matrix compression and supports distributed deployment and multi-task knowledge coexistence. By jointly optimizing knowledge distillation, low-rank modeling, distributed knowledge allocation, and integer linear programming with greedy approximation, DeKAP significantly reduces communication overhead and computational resource consumption while preserving alignment fidelity. Experimental results demonstrate that DeKAP achieves superior overall performance compared to state-of-the-art approaches.

Technology Category

• Multiagent Systems: Distributed Problem Solving
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Future networks are envisioned to connect massive artificial intelligence (AI) agents, enabling their extensive collaboration on diverse tasks. Compared to traditional entities, these agents naturally suit the semantic communication (SC), which can significantly enhance the bandwidth efficiency. Nevertheless, SC requires the knowledge among agents to be aligned, while agents have distinct expert knowledge for their individual tasks in practice. In this paper, we propose a distillation-enabled knowledge alignment protocol (DeKAP), which distills the expert knowledge of each agent into parameter-efficient low-rank matrices, allocates them across the network, and allows agents to simultaneously maintain aligned knowledge for multiple tasks. We formulate the joint minimization of alignment loss, communication overhead, and storage cost as a large-scale integer linear programming problem and develop a highly efficient greedy algorithm. From computer simulation, the DeKAP establishes knowledge alignment with the lowest communication and computation resources compared to conventional approaches.

Problem

• Aligning distinct expert knowledge across AI agents
• Minimizing communication overhead and storage costs
• Enabling efficient semantic communication for multi-task collaboration

Innovation

• Distills expert knowledge into low-rank matrices
• Allocates knowledge matrices across agent networks
• Solves optimization via efficient greedy algorithm