Defining Decentralization: An Ontological Perspective

2026-08-10

AI Summary

This study addresses the absence of a universal, domain-agnostic definition of decentralization across computer communication systems, which has led to inconsistent analyses, incommensurable comparisons, and imprecise system designs. From an ontological perspective, the work models decentralization as a relational and observer-dependent property, introducing the first domain-independent graph-based ontological framework that clearly distinguishes decentralization from distribution. The framework incorporates two novel metrics—Void Tolerance and Imperviousness—and leverages formal semantics alongside browser-based automated reasoning to enable quantitative evaluation and classification of decentralization. Validation through case studies in federated learning and blockchain demonstrates that the framework yields consistent, comparable, and logically coherent assessments.

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.

Problem

• decentralization
• computer communication systems
• definition ambiguity
• distributed AI
• ontology

Innovation

• decentralization ontology
• graph-based modeling
• Void Tolerance
• Imperviousness
• formal semantics