Holonic Active Distillation for Scalable Multi-Agent Learning in Multi-Sensor Systems

2026-06-30

AI Summary

This work addresses the challenges of scalability, adaptability, and knowledge transfer in multi-sensor systems arising from dynamic addition or removal of subsystems. To this end, it proposes the Holographic Active Distillation (HAD) framework, which integrates streaming active distillation with sensor similarity clustering to enable local student models to interact with a teacher model via pseudo-label queries, thereby balancing local specialization and global generalization. Embedded within a Holographic Multi-Agent System (HMAS), the approach supports incremental updates and dynamic knowledge sharing, overcoming the static structural limitations of conventional multi-agent learning paradigms. Experimental results demonstrate that the system significantly enhances learning efficiency and robustness in open environments, validating the scalability advantages of holographic learning in dynamic scenarios and revealing the critical impact of model drift on long-term adaptability.

Abstract

The rapid expansion of sensor-based networks introduces major challenges in scalability, adaptability, and knowledge transfer, especially in open environments where new subsystems can dynamically join or leave. In this work, we propose a Holonic Active Distillation architecture within a Holonic Multi-Agent System (HMAS) to address these issues. Our approach integrates Clustered Stream-Based Active Distillation (CSBAD), a framework in which specialized student models collect local data, query pseudo-labels from teacher models, and cluster into groups of similar sensors. Results show that the holonic organization balances local specialization with global generalization, while efficiently adapting to sensor departures and re-integrations. We also analyzed trade-offs among incremental model updates, system reorganization, and scalability limits. Our findings highlight the advantages of holonic learning for multi-sensor systems while identifying key challenges related to model drift and long-term adaptation.

Problem

• scalability
• adaptability
• knowledge transfer
• multi-sensor systems
• open environments

Innovation

• Holonic Multi-Agent System
• Active Distillation
• Multi-Sensor Learning
• Scalable AI
• Model Clustering