Trust Semantics Distillation for Collaborator Selection via Memory-Augmented Agentic AI
This work addresses the challenge of efficient and accurate trustworthiness assessment for device collaboration in computing systems, representing an incremental improvement over existing methods.
The paper tackles the problem of high overhead and inaccurate trust evaluation in collaborator selection for complex computing tasks by proposing a task-specific trust semantics distillation model, which reduces evaluation time, decreases device resource consumption, and improves selection accuracy.
Accurate trustworthiness evaluation of potential collaborating devices is essential for the effective execution of complex computing tasks. This evaluation process involves collecting diverse trust-related data from potential collaborators, including historical performance and available resources, for collaborator selection. However, when each task owner independently assesses all collaborators' trustworthiness, frequent data exchange, complex reasoning, and dynamic situation changes can result in significant overhead and deteriorated trust evaluation. To overcome these challenges, we propose a task-specific trust semantics distillation (2TSD) model based on a large AI model (LAM)-driven teacher-student agent architecture. The teacher agent is deployed on a server with powerful computational capabilities and an augmented memory module dedicated to multidimensional trust-related data collection, task-specific trust semantics extraction, and task-collaborator matching analysis. Upon receiving task-specific requests from device-side student agents, the teacher agent transfers the trust semantics of potential collaborators to the student agents, enabling rapid and accurate collaborator selection. Experimental results demonstrate that the proposed 2TSD model can reduce collaborator evaluation time, decrease device resource consumption, and improve the accuracy of collaborator selection.