Enhancing Privacy and Security in Decentralized Healthcare with Federated Learning and Blockchain

Abstract

The adoption of Industry 4.0 technology in healthcare has led to increased concerns about data privacy, security, and interoperability. Traditional centralized healthcare systems are vulnerable to cybercriminal and unauthorized access; thus there is a need for secure and privacypreserving data sharing architecture. This work proposes an integration of Federated Learning (FL) with Blockchain to deliver improvements in the security, scalability, and privacy of decentralized healthcare. Florida allows hospitals to train their models locally and share them without divulging raw patient data, whereas Blockchain ensures data security, decentralized identity management, and safe access control through smart contracts. Although providing such high-level security requires the usage of advanced techniques such as Zero-Knowledge Proofs (ZKP), Homomorphic Encryption, and Proof-of-Stake (PoS) consensus, the high protection level of data is definitely on the huge-scale advantages of blockchain technology. The proposed model is in line with Industry 4.0 concepts that support automation, interoperability, and strong data ecosystems in healthcare. The resolve is to reduce security threats ensure regulatory compliance and exchange healthcare data with the highest standards of security. Experimental results show improved security, privacy, and efficiency, which make this solution a scalable and robust alternative for current decentralized healthcare data management in Industry 4.0.