AI Insight
This paper addresses the lack of a universal definition for "decentralization" in computer science by proposing a formal graph-based ontology that treats decentralization as a relational and subject-specific property of computer communication systems. The authors introduce two novel metrics—Void Tolerance and Imperviousness—to quantitatively evaluate decentralization and provide a browser-based tool for automated system classification. Applications to federated learning and blockchain architectures demonstrate that this framework produces more consistent assessments than existing informal definitions.
Why it matters
As decentralized systems become central to AI, blockchain, and distributed computing, having a rigorous, standardized definition enables better system comparison, more reliable security analysis, and improved protocol design. This framework could facilitate clearer communication among researchers and practitioners working across different domains where decentralization is a design goal.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
-cross
Abstract: 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.
Source: Defining Decentralization: An Ontological Perspective