Modern distributed decision-making systems face significant challenges arising from data heterogeneity, dynamic environments, and the need for decentralized coordination. This paper introduces the Knowledge Sharing paradigm as an innovative approach that uses the semantic richness of Knowledge Graphs (KGs) and the representational power of Graph Embeddings (GEs) to achieve decentralized intelligence. Our architecture empowers individual nodes to locally construct semantic representations of their operational context, iteratively aggregating embeddings through neighbor-based exchanges using GraphSAGE. This iterative local aggregation process results in a dynamically evolving global semantic abstraction called Knowledge Map, enabling coordinated decision-making without centralized control. To validate our approach, we conduct extensive experiments under a distributed resource orchestration use case. We simulate different network topologies and node workloads, analyzing the local semantic drift of individual nodes. Experimental results confirm that our distributed knowledge-sharing mechanism effectively maintains semantic coherence and adaptability, making it suitable for complex and dynamic environments such as Edge Computing, IoT, and multi-agent systems.
Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Qatar University（卡塔尔大学）；Delft University of Technology（代尔夫特理工大学）；Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学（MBZUAI））
Resilient by Design -- Active Inference for Distributed Continuum Intelligence
Department of Computer Systems and Sciences, Stockholm University（斯德哥尔摩大学计算机系统与科学系）；Department of Information Engineering, University of Pisa（比萨大学信息工程系）；Distributed Systems Group, TU Wien（维也纳技术大学分布式系统组）
Agentic Fog: A Policy-driven Framework for Distributed Intelligence in Fog Computing
Sarhad University of Science & Information Technology（萨赫德科学与技术大学）；GIK Institute of Engineering Sciences & Technology（吉克工程科学与技术学院）；University of Essex（埃塞克斯大学）
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
Division of Natural and Applied Sciences, Duke Kunshan University（杜克-昆山大学自然科学与应用科学系）；Department of Electrical and Computer Engineering, The University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）；College of Future Information Technology, Fudan University（复旦大学未来信息科技学院）；Ant Group（蚂蚁集团）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）
