Decentralized governance and artificial intelligence policy with blockchain-based voting in federated learning

C Alisdair Lee; K M Chow; H Anthony Chan; Daniel Pak-Kong Lun

doi:10.3389/frma.2023.1035123

Decentralized governance and artificial intelligence policy with blockchain-based voting in federated learning

Front Res Metr Anal. 2023 Feb 16:8:1035123. doi: 10.3389/frma.2023.1035123. eCollection 2023.

Authors

C Alisdair Lee^{1

2}, K M Chow³, H Anthony Chan¹, Daniel Pak-Kong Lun²

Affiliations

¹ School of Computing and Information Sciences, Caritas Institute of Higher Education, Hong Kong SAR, China.
² Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.
³ School of Business and Hospitality Management, Caritas Institute of Higher Education, Hong Kong SAR, China.

Abstract

Introduction: Fruit losses in the supply chain owing to improper handling and a lack of proper control are common in the industry. As losses are caused by the inefficiency of the export method, selecting the appropriate export method is a possible solution. Several organizations employ only a single strategy, which is mainly based on a first-in-first-out approach. Such a policy is easy to manage but inefficient. Given that the batch of fruits may become overripe during transportation, frontline operators do not have the authority or immediate support to change the fruit dispatching strategy. Thus, this study aims to develop a dynamic strategy simulator to determine the sequence of delivery based on forecasting information projected from probabilistic data to reduce the amount of fruit loss.

Methods: The proposed method to accomplish asynchronous federated learning (FL) is based on blockchain technology and a serially interacting smart contract. In this method, each party in the chain updates its model parameters and uses a voting system to reach a consensus. This study employs blockchain technology with smart contracts to serially enable asynchronous FL, with each party in the chain updating its parameter model. A smart contract combines a global model with a voting system to reach a common consensus. Its artificial intelligence (AI) and Internet of Things engine further strengthen the support for implementing the Long Short-Term Memory (LSTM) forecasting model. Based on AI technology, a system was constructed using FL in a decentralized governance AI policy on a blockchain network platform.

Results: With mangoes being selected as the category of fruit in the study, the system improves the cost-effectiveness of the fruit (mango) supply chain. In the proposed approach, the simulation outcomes show fewer mangoes lost (0.035%) and operational costs reduced.

Discussion: The proposed method shows improved cost-effectiveness in the fruit supply chain through the use of AI technology and blockchain. To evaluate the effectiveness of the proposed method, an Indonesian mango supply chain business case study has been selected. The results of the Indonesian mango supply chain case study indicate the effectiveness of the proposed approach in reducing fruit loss and operational costs.

Keywords: EVM compatible smart contract; Internet of Things cost-efficiency; blockchain; fruit supply chain simulation; immutable federated learning forecasting norm; intelligent agent risk management; machine learning.

Grants and funding

This work was supported by the Institutional Developmental Grant of the Caritas Institute of Higher Education (IDG220221P) and the Research Grants Council of the Hong Kong Special Administrative Region, China, under Grant No. UGC/IDS(R)11/19 for AI facility and manpower support.