# Defining Decentralization: An Ontological Perspective

arXiv: https://arxiv.org/abs/2608.09748 · HTML: https://mlsanity.com/paper/2608.09748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 54 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Secure Aggregation for Federated Learning in Flower](https://mlsanity.com/paper/2205.06117/) (2022)
- [Efficient and Less Centralized Federated Learning](https://mlsanity.com/paper/2106.06627/) (2021)
- [SoK: Blockchain-Based Decentralized AI (DeAI)](https://mlsanity.com/paper/2411.17461/) (2024)
- [Decentralization in Bitcoin and Ethereum Networks](https://mlsanity.com/paper/1801.03998/) (2018)
- [Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments](https://mlsanity.com/paper/1704.08065/) (2017)
- [Ontology Modeling for Decentralized Household Energy Systems](https://mlsanity.com/paper/2108.01504/) (2021)
- [VeriLLM: A Lightweight Framework for Publicly Verifiable Decentralized Inference](https://mlsanity.com/paper/2509.24257/) (2025)
- [Impossibility of Full Decentralization in Permissionless Blockchains](https://mlsanity.com/paper/1905.05158/) (2019)
- [Measuring Decentralization in Bitcoin and Ethereum using Multiple Metrics and Granularities](https://mlsanity.com/paper/2101.10699/) (2021)
- [Cross-Referencing Method for Scalable Public Blockchain](https://mlsanity.com/paper/2107.12981/) (2021)
- [The Concept of Decentralization Through Time and Disciplines: A Quantitative Exploration](https://mlsanity.com/paper/2207.14260/) (2022)
- [A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning](https://mlsanity.com/paper/2503.09833/) (2025)

## Abstract (arXiv)

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.