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Source note3 min read

Defining Decentralization: An Ontological Perspective

Source

arXiv

arxiv.org/abs/2608.09748v1

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Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Jakub Kacper Szeląg, Aydin Abadi, Mohammad Naseri
  • Venue: arXiv: cs.DC
  • Published: 2026-08-10
  • Collected: 2026-08-11
  • URL: http://arxiv.org/abs/2608.09748v1
  • PDF: https://arxiv.org/pdf/2608.09748v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

The authors address the lack of a universally accepted definition of decentralization in computer science, particularly in emerging fields like decentralized AI and machine learning. They propose a graph-based ontology to define decentralization as a relational and subject-specific property of computer communication systems, distinguish it from distribution, and introduce two new metrics for evaluation: Void Tolerance and Imperviousness.

What the Document Says

  • The concept of decentralization in computer science has been around for over half a century.
  • There is no universally accepted definition of decentralization applicable across all computer communication systems.
  • This lack of definition becomes more problematic with the emergence of decentralized AI and machine learning paradigms.
  • Existing approaches often 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.
  • The authors refer to this issue as the Decentralization Problem.
  • They analyze the foundations of decentralization from a formal-semantic, epistemological, and pragmatic perspective.
  • A graph-based ontology is introduced to define decentralization as both relational and subject-specific property of computer communication systems.
  • The framework formally distinguishes decentralization from distribution.
  • Two novel metrics are provided for evaluation: Void Tolerance and Imperviousness.
  • An implementation allowing automated classification and metric computation of arbitrary systems is also provided.
  • Instantiations to federated learning and blockchain architectures demonstrate consistent, comparable assessments.
  • Existing definitions produce incomplete or contradictory conclusions in these domains.

Key Concepts Mentioned

  • decentralization
  • computer science
  • definition
  • AI
  • machine learning
  • distribution
  • graph-based ontology
  • Void Tolerance
  • Imperviousness

Claims Made by the Document

  • There is a lack of a universally accepted definition of decentralization in computer science.
  • Existing approaches often conflate decentralization with related notions.
  • The proposed framework distinguishes decentralization from distribution.

Evidence Used

  • Lack of consistent definitions in the text
  • Descriptions of conflation between concepts
  • Introduction and explanation of the new framework

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.