Holonic Active Distillation for Scalable Multi-Agent Learning in Multi-Sensor Systems
For multi-sensor systems facing scalability and adaptability challenges, this work introduces a holonic learning approach, though it is incremental and identifies remaining challenges.
The paper proposes a Holonic Active Distillation architecture for scalable multi-agent learning in multi-sensor systems, demonstrating that it balances local specialization with global generalization and adapts to sensor changes. No concrete numbers are provided.
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.