Decentralized governance and artificial intelligence policy with blockchain-based voting in federated learning - Lee, C. Alisdair; Chow, K. M.; Chan, H. Anthony; Lun, Daniel Pak-Kong (Frontiers in Research Metrics and Analytics, 2023)
Fruit losses in the supply chain owing to improper handling and 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 approach . 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 is inefficient. Considering that the batch of fruits may be exposed to a risk of being overripe during transportation, frontline operators do not have the authority or instant support for changing the strategy in fruit dispatching. 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. The proposed method is based on blockchain technology and a serial interacting smart contract to accomplish asynchronous federated learning (FL). In this method, each party in the chain updates its model parameters and uses a voting system to reach a consensus. This study uses blockchain technology with smart contract to enable asynchronous FL serially, where each party in the chain updates its parameter model. A smart contract groups a global model with a voting system to arrive at a common consensus. Its artificial intelligence (AI) and an Internet of Things engine further strengthens the support in implementing the long short-term memory forecasting model. Based on AI technology, a system was constructed using FL in a decentralized governance AI policy on a blockchain network platform. With mangoes being selected as the category of fruit in the study, the system improves the cost-effectiveness in the fruit (mango) supply chain. In the proposed approach, the simulation outcomes have fewer mangoes lost (0.035%) with economical operational expenses. To evaluate the effectiveness of the proposed method, a case study of the mango supply chain business in Indonesia is selected.