FusionFedBlock: Fusion of blockchain and federated learning to preserve privacy in industry 5.0

• Fusion of Blockchain and Federated Learning to Preserve Privacy in Industry 5.0. • Design federated learning network for privacy preserved data flow in an Industrial Environment. • Provide decentralized secure storage by the Distributed Hash Table (DHT) at the cloud. • Evaluate the proposed scheme with Blockchain and Federated Learning. • Comparison of our novel work with existing research as accuracy. Nowadays, Industries are experiencing rapid changes in the digital environment, referred to as Industry 5.0. The Internet of Things (IoT) and advanced technologies are essential in the industrial environment. Technological advancements can collect, transfer, and analyze vast amounts of data in the industry via promising technologies. Still, IoT has various issues when applied to industrial infrastructures, such as centralization, privacy preservation, latency, and security. This article proposes a scheme as FusionFedBlock: Fusion of Blockchain and Federated Learning to Preserve Privacy in Industry 5.0 to address the aforementioned issues. At the federated layer, the industry's departments (Production, Quality Control, Distribution) allow local learning updates with network automation and communicate to the global model, which miners verify in the Blockchain networks. Federated-Learning offers privacy preservation between various mentioned departments in industries. Decentralized secure storage is provided by the Distributed Hash Table (DHT) at the cloud layer. The validation outcomes of the proposed scheme demonstrate excellent performance as the accuracy of 93.5% in a 50% active node for Industry 5.0 compared to existing frameworks.