Centralised or Decentralised? Data Analysis of Transaction Network of Hedera Hashgraph
- URL: http://arxiv.org/abs/2311.06865v1
- Date: Sun, 12 Nov 2023 14:52:32 GMT
- Title: Centralised or Decentralised? Data Analysis of Transaction Network of Hedera Hashgraph
- Authors: Lucas Amherd, Sheng-Nan Li, Claudio J. Tessone,
- Abstract summary: This study expands the current literature by offering a first-time, data-driven analysis of the degree of decentralisation of the platform Hedera Hashgraph.
Results show a considerably higher amount of released supply compared to the release schedule and a growing number of daily active accounts.
Hedera Hashgraph exhibits a high centralisation of wealth and a shrinking core that acts as an intermediary for the rest of the network.
- Score: 0.12289361708127873
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: An important virtue of distributed ledger technologies is their acclaimed higher level of decentralisation compared to traditional financial systems. Empirical literature, however, suggests that many systems tend towards centralisation as well. This study expands the current literature by offering a first-time, data-driven analysis of the degree of decentralisation of the platform Hedera Hashgraph, a public permissioned distributed ledger technology, employing data directly fetched from a network node. The results show a considerably higher amount of released supply compared to the release schedule and a growing number of daily active accounts. Also, Hedera Hashgraph exhibits a high centralisation of wealth and a shrinking core that acts as an intermediary in transactions for the rest of the network. However, the Nakamoto index and Theil index point to recent progress towards a more decentralised network.
Related papers
- XDC Network Assessment: Decentralization, Scalability and Security [0.0]
XinFin, in 2019, unveiled the XDC network, an enterprise-ready hybrid blockchain platform that is open-source and specializes in tokenization for real-world decentralized finance.
Overseeing the XDC network is the XDC Foundation, a non-profit organization established to encourage the growth, enhancement, and adoption of the XDC Network.
arXiv Detail & Related papers (2024-08-05T09:01:43Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Analyzing Reward Dynamics and Decentralization in Ethereum 2.0: An
Advanced Data Engineering Workflow and Comprehensive Datasets for
Proof-of-Stake Incentives [5.18461573800406]
Smart contract blockchain platform, Proof-of-Stake 2.0, guarantees precise execution of applications without third-party intervention.
Our study collects consensus reward data from the Beacon chain and conducts a comprehensive analysis of reward distribution and evolution.
To evaluate the degree of decentralization in PoS, we apply several inequality indices, including the Shannon entropy, the Gini Index, the Nakamoto Coefficient, and the Herfindahl-Hirschman Index (HHI)
arXiv Detail & Related papers (2024-02-17T02:40:00Z) - 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) - Optimizing Closed Payment Networks on the Lightning Network: Dual Central Node Approach [0.0]
The Lightning Network, known for its millisecond settlement speeds and low transaction fees, offers a compelling alternative to traditional payment processors.
This is particularly significant for the unbanked population, which lacks access to standard financial services.
Our research targets businesses looking to shift their client to client payment processes, such as B2B invoicing, remittances, and cross-border transactions, to the Lightning Network.
arXiv Detail & Related papers (2023-12-06T21:35:19Z) - 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) - RelaySum for Decentralized Deep Learning on Heterogeneous Data [71.36228931225362]
In decentralized machine learning, workers compute model updates on their local data.
Because the workers only communicate with few neighbors without central coordination, these updates propagate progressively over the network.
This paradigm enables distributed training on networks without all-to-all connectivity, helping to protect data privacy as well as to reduce the communication cost of distributed training in data centers.
arXiv Detail & Related papers (2021-10-08T14:55:32Z) - 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) - 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) - Decentralized Learning for Channel Allocation in IoT Networks over
Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game [134.88020946767404]
We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network.
Our study maps this problem into a contextual multi-player, multi-armed bandit game, and proposes a purely decentralized, three-stage policy learning algorithm through trial-and-error.
arXiv Detail & Related papers (2020-03-30T10:05:35Z) - Quantized Decentralized Stochastic Learning over Directed Graphs [52.94011236627326]
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.