The field of blockchain and digital twins is rapidly advancing, with a focus on improving security, scalability, and transparency. Researchers are exploring the use of blockchain technology to enhance the security and integrity of digital twins, particularly in applications such as voting systems, federated learning, and digital identities. The development of redactable blockchains, decentralized weather forecasting, and transparent fairness evaluation protocols are also notable trends. Additionally, digital twins are being used to improve decision-making in agriculture, construction, and other industries. Noteworthy papers include: Secure and Scalable Blockchain Voting, which presents a comparative framework for analyzing blockchain-based E-Voting architectures; Blockchain-Enabled Federated Learning, which provides a comprehensive architectural analysis of BCFL systems; and AlDBaran, which introduces a novel authenticated data structure for blockchains.
Blockchain and Digital Twin Advancements
Sources
Secure and Scalable Blockchain Voting: A Comparative Framework and the Role of Large Language Models
Blockchain-Enabled Federated Learning
On the Operational Resilience of CBDC: Threats and Prospects of Formal Validation for Offline Payments
Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions
Developing a Calibrated Physics-Based Digital Twin for Construction Vehicles
Redactable Blockchains: An Overview
Decentralized Weather Forecasting via Distributed Machine Learning and Blockchain-Based Model Validation
A Transparent Fairness Evaluation Protocol for Open-Source Language Model Benchmarking on the Blockchain
FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
An Architecture for Distributed Digital Identities in the Physical World
Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations
AlDBaran: Towards Blazingly Fast State Commitments for Blockchains