让每个边缘设备自主学习并智能协作，打破中心化瓶颈
Node Learning: A Framework for Adaptive, Decentralised and Collaborative Network Edge AI
- 边缘节点自主学习，仅在必要时与邻居交换知识
- 无需全局同步，通过局部交互实现知识扩散
- 适合移动、资源受限的异构网络环境
随着AI向边缘扩展，集中式智能的代价与脆弱性日益凸显。数据传输、延迟、能耗及对大型数据中心的依赖，在异构、移动、资源受限的环境中难以规模扩展。本文提出节点学习（Node Learning），一种去中心化的学习范式：智能驻留于单个边缘节点，通过选择性邻接互动逐步扩展。节点持续从本地数据学习，维护自身模型状态，并在合作有益时机会性地交换知识。学习通过重叠与扩散传播，而非全局同步或中心聚合。该范式统一了自主与协作行为，兼容数据、硬件、目标和连接性的异质性。本文构建其概念基础，对比现有去中心化方法，并探讨对通信、硬件、信任与治理的影响。节点学习并非抛弃现有范式，而是将其置于更广泛的去中心化视角中。
原文摘要 · Abstract (English)
The expansion of AI toward the edge increasingly exposes the cost and fragility of cen- tralised intelligence. Data transmission, latency, energy consumption, and dependence on large data centres create bottlenecks that scale poorly across heterogeneous, mobile, and resource-constrained environments. In this paper, we introduce Node Learning, a decen- tralised learning paradigm in which intelligence resides at individual edge nodes and expands through selective peer interaction. Nodes learn continuously from local data, maintain their own model state, and exchange learned knowledge opportunistically when collaboration is beneficial. Learning propagates through overlap and diffusion rather than global synchro- nisation or central aggregation. It unifies autonomous and cooperative behaviour within a single abstraction and accommodates heterogeneity in data, hardware, objectives, and connectivity. This concept paper develops the conceptual foundations of this paradigm, contrasts it with existing decentralised approaches, and examines implications for communi- cation, hardware, trust, and governance. Node Learning does not discard existing paradigms, but places them within a broader decentralised perspective
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