发表机构
Flower Labs（Flower Labs）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对计算机领域缺乏通用去中心化定义的问题，提出基于图的本体框架，区分去中心化与分布性，引入两个新指标并实现浏览器工具，为联邦学习等系统提供一致的去中心化评估基础。
AI 中文摘要
去中心化作为计算机科学中的一个概念已存在超过半个世纪。尽管它在安全、分布式计算、人工智能、云基础设施和物联网（IoT）架构等领域发挥着基础性作用，但目前仍没有适用于所有计算机通信系统的普遍接受的去中心化定义。随着去中心化人工智能和机器学习范式的出现，包括协同训练、分布式推理、基于区块链的范式以及智能体AI，去中心化常被视为核心设计目标，这一问题愈发凸显。同时，现有方法常将去中心化与信任分布或特定实现范式等相关概念混淆，这种模糊性导致系统分析出现不一致，限制了不同研究工作间的可比性，并削弱了围绕通信架构和协议设计的形式化推理的严谨性。本研究将这一研究空白定义为去中心化问题。我们分析了去中心化的形式语义学、认识论和实用基础，引入了一种基于图的本体，将去中心化定义为计算机通信系统的关系性和主体特定属性。该框架将去中心化与分布性正式区分，并通过两个新指标：空值容忍度（Void Tolerance）和抗扰性（Imperviousness）进行评估。我们还提供了一个基于浏览器的实现，可对任意系统进行自动分类和指标计算。对联邦学习和区块链架构的实例化表明，在现有定义产生不完整或矛盾结论的场景中，该方法能提供一致、可比的评估，为分析异构系统中的去中心化提供了与领域无关的基础。
英文摘要
Decentralization as a concept in computer science has existed for over half a century. Despite its fundamental role across domains such as security, distributed computing, artificial intelligence, cloud infrastructures, and Internet of Things (IoT) architectures, there remains no universally accepted definition of decentralization applicable across computer communication systems. This has become increasingly problematic with the emergence of decentralized AI and machine learning paradigms, including collaborative training, distributed inference, blockchain-based, and agentic AI, where decentralization is often treated as a core design objective. Meanwhile, existing approaches frequently conflate decentralization with related notions such as distribution of trust or specific implementation paradigms. Such ambiguity creates inconsistencies in system analysis, limits comparability between works, and weakens the rigor of formal reasoning surrounding communication architectures and protocol design. In this work, we define this research gap as the Decentralization Problem. We analyze the formal-semantic, epistemological, and pragmatic foundations of decentralization and introduce a graph-based ontology defining it as both relational and subject-specific property of computer communication systems. The framework formally distinguishes decentralization from distribution and supports evaluation through two novel metrics: Void Tolerance and Imperviousness. We also provide a browser-based implementation that enables automated classification and metric computation of arbitrary systems. Instantiations to federated learning and blockchain architectures show consistent, comparable assessments where existing definitions produce incomplete or contradictory conclusions, providing a domain-independent foundation for analysing decentralization across heterogeneous systems.
Comments27 pages, 6 figures, preparing for submission, strengthened the formalisms behind ontological claims
