Blockchain-Driven Federated Learning Based Differential Privacy in Decentralized Supply Chain

Nowadays, supply chain system has been used more, but there are some issues regarding the data privacy, transparency, and the decentralization for the different suppliers. Earlier centralized methods having the challenges in incorporating the security and efficiency which can add the more complexity in their data. This paper proposed the solution to these different issues by using the Blockchain-Driven Federated Learning Framework augmented with Differential Privacy which can provide the security against the confidential data along with the decentralized supply chain system. This technique uses the properties of the blockchain technology which provides the secure and transparent ledger that can provide the trust and traceability amongst the participants of the supply chain system. Federated learning is an approach which can allow us to train the model in the collaboration at the different locations and for that we don't need to exchange the data. Enhancing the risk of privacy issue in more appropriate way we can use the differential privacy technique incorporating with Interplanetary File System (IPFS). This file system with blockchain technology can enable the system architecture to provide the scalability in the storage and trustworthiness amongst the different stakeholders. Smart contracts streamline the model aggregation and consensus processes, enabling collaborative efforts without compromising data privacy.