SoK: Measuring Blockchain Decentralization

• URL: http://arxiv.org/abs/2501.18279v2
• Date: Thu, 06 Feb 2025 11:47:10 GMT
• Title: SoK: Measuring Blockchain Decentralization
• Authors: Christina Ovezik, Dimitris Karakostas, Mary Milad, Aggelos Kiayias, Daniel W. Woods,
• Abstract summary: In the context of blockchain systems, the importance of decentralization is undermined by the lack of a widely accepted methodology to measure it. We put forth a framework that categorizes all measurement techniques used in previous work based on the resource they target. We complement this framework with an empirical analysis designed to evaluate whether the various pre-processing steps and metrics used in prior work capture the same underlying concept of decentralization.
• Score: 7.274273862904251
• License:
• Abstract: In the context of blockchain systems, the importance of decentralization is undermined by the lack of a widely accepted methodology to measure it. To address this gap, we set out a systematization effort targeting the decentralization measurement workflow. To facilitate our systematization, we put forth a framework that categorizes all measurement techniques used in previous work based on the resource they target, the methods they use to extract resource allocation, and the functions they apply to produce the final measurements. We complement this framework with an empirical analysis designed to evaluate whether the various pre-processing steps and metrics used in prior work capture the same underlying concept of decentralization. Our analysis brings about a number of novel insights and observations. First, the seemingly innocuous choices performed during data extraction, such as the size of estimation windows or the application of thresholds that affect the resource distribution, have important repercussions when calculating the level of decentralization. Second, exploratory factor analysis suggests that in Proof-of-Work (PoW) blockchains, participation on the consensus layer is not correlated with decentralization, but rather captures a distinct signal, unlike in Proof-of-Stake (PoS) systems, where the different metrics align under a single factor. These findings challenge the long-held assumption within the blockchain community that higher participation drives higher decentralization. Finally, we combine the results of our empirical analysis with first-principles reasoning to derive practical recommendations for researchers that set out to measure blockchain decentralization, and we further systematize the existing literature in line with these recommendations.

Related papers

• How Does Stake Distribution Influence Consensus? Analyzing Blockchain Decentralization [10.679753825744964]
  This study first formalizes decentralization metrics for weighted consensus mechanisms. We introduce the Square Root Stake Weight (SRSW) model, which effectively recalibrates staking weight distribution. This research is a pivotal step toward a more fair and equitable distribution of staking weight, advancing the decentralization in blockchain consensus mechanisms.
  arXiv  Detail & Related papers  (2023-12-21T15:32:20Z)
• Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach [47.00450933765504]
  We propose the use of explainable artificial intelligence for decentralised computing of estimated contributions to a metric of interest. This approach mitigates the need to convince supply chain actors to share data, as all computations occur in a decentralised manner. Results demonstrate the effectiveness of our approach in detecting the source of quality variations compared to a centralised approach.
  arXiv  Detail & Related papers  (2023-07-22T20:03:16Z)
• Networked Communication for Decentralised Agents in Mean-Field Games [59.01527054553122]
  We introduce networked communication to the mean-field game framework. We prove that our architecture has sample guarantees bounded between those of the centralised- and independent-learning cases.
  arXiv  Detail & Related papers  (2023-06-05T10:45:39Z)
• SoK: A Stratified Approach to Blockchain Decentralization [6.66161432273916]
  We put forth a systematization of the current landscape with respect to decentralization. Our approach dissects blockchain systems into multiple layers, or strata, each possibly encapsulating multiple categories. We introduce a practical test, the "Minimum Decentralization Test" which can provide quick insights about the decentralization state of a blockchain system.
  arXiv  Detail & Related papers  (2022-11-02T17:13:09Z)
• Learning from Heterogeneous Data Based on Social Interactions over Graphs [58.34060409467834]
  This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions. We show that the. strategy enables the agents to learn consistently under this highly-heterogeneous setting. We show that the. strategy enables the agents to learn consistently under this highly-heterogeneous setting.
  arXiv  Detail & Related papers  (2021-12-17T12:47:18Z)
• Consensus Control for Decentralized Deep Learning [72.50487751271069]
  Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. We show in theory that when the training consensus distance is lower than a critical quantity, decentralized training converges as fast as the centralized counterpart. Our empirical insights allow the principled design of better decentralized training schemes that mitigate the performance drop.
  arXiv  Detail & Related papers  (2021-02-09T13:58:33Z)
• Dif-MAML: Decentralized Multi-Agent Meta-Learning [54.39661018886268]
  We propose a cooperative multi-agent meta-learning algorithm, referred to as MAML or Dif-MAML. We show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting.
  arXiv  Detail & Related papers  (2020-10-06T16:51:09Z)
• Taxonomy of Centralization in Public Blockchain Systems: A Systematic Literature Review [2.1315215140430683]
  Bitcoin introduced delegation of control over a monetary system from a select few to all who participate in that system. This delegation is known as the decentralization of controlling power and is a powerful security mechanism for the ecosystem. Recent studies have observed a trend of increased centralization in cryptocurrencies such as Bitcoin and Governance.
  arXiv  Detail & Related papers  (2020-09-26T08:58:48Z)
• A general framework for decentralized optimization with first-order methods [11.50057411285458]
  Decentralized optimization to minimize a finite sum of functions over a network of nodes has been a significant focus in control and signal processing research. The emergence of sophisticated computing and large-scale data science needs have led to a resurgence of activity in this area. We discuss decentralized first-order gradient methods, which have found tremendous success in control, signal processing, and machine learning problems.
  arXiv  Detail & Related papers  (2020-09-12T17:52:10Z)
• Quantized Decentralized Stochastic Learning over Directed Graphs [54.005946490293496]
  We consider a decentralized learning problem where data points are distributed among computing nodes communicating over a directed graph. As the model size gets large, decentralized learning faces a major bottleneck that is the communication load due to each node transmitting messages (model updates) to its neighbors. We propose the quantized decentralized learning algorithm over directed graphs that is based on the push-sum algorithm in decentralized consensus optimization.
  arXiv  Detail & Related papers  (2020-02-23T18:25:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.