An Overview of Discoverable and Reusable Intelligence for Collaborative Edge Devices Without Full Model Transfer

Abstract

The rapid growth of Artificial Intelligence (AI), Internet of Things (IoT), and edge computing has transformed the development of intelligent applications across healthcare, transportation, manufacturing, agriculture, and smart cities. Although cloud-based AI provides powerful computational capabilities, it often suffers from high latency, bandwidth limitations, privacy concerns, and dependency on continuous internet connectivity. Edge AI addresses these limitations by performing computation closer to where data is generated. However, conventional collaborative learning techniques such as Federated Learning still require repeated exchange of complete model parameters, resulting in high communication overhead and increased resource consumption.

Recent research has introduced the concept of discoverable and reusable intelligence, where edge devices collaborate by exchanging reusable knowledge instead of transferring complete AI models. This paradigm enables devices to discover suitable knowledge sources and reuse learned intelligence through techniques such as knowledge distillation, prototype sharing, and lightweight transfer learning. As a result, communication cost is reduced while maintaining privacy, scalability, and learning performance.

This review paper presents an overview of discoverable and reusable intelligence for collaborative edge devices without full model transfer. It discusses the evolution of collaborative Edge AI, its architecture, major application areas, existing challenges, research gaps, and future research directions. The paper also highlights how this emerging paradigm can improve collaboration among heterogeneous edge devices and support the development of efficient and intelligent distributed AI systems.