This project aims to research, design and evaluate zero-knwledge machine learning (ZKML) models for blockchain-based decentralized energy trading. The inherent transparency of blockchain raises concerns about user privacy, which can lead to sensitive energy data leakage. Additionally, the cost of on-chain computation and the limitations of smart contracts hinder the integration of advanced artificial intelligence technologies. In this project, we propose to leverage blockchain and ZKML technologies to design a novel privacy-preserving and intelligent solution for peer-to-peer energy trading. This solution contains three potential models (or structures), i.e., centralized, decentralized, and federated ZKML from the architectural perspective.
This project aims to research, design and evaluate a dependable decentralized computing and communication platform to support functional and nonfunctional requirements of transactive microgrids with the focus of renewable energies. More specifically, we propose to analyze, leverage and extend DAG-based and chain-based Blockchains, smart contracts and self-adaptive paradigm to realize the proposed platform. Currently, we are building, modelling and optimizing a blockchain-based Web3 platform which aims to ficilitate the peer-to-peer energy trading which will have a great impact on renewable energy industry.