SECURE FEDERATED LEARNING ARCHITECTURE UTILIZING BLOCKCHAIN AND ADVANCED CRYPTOGRAPHY WITH BYZANTINE FAULT TOLERANCE FOR DECENTRALIZED DATA INTEGRITY

Abstract

This paper provides a Protect Federated Learning Structure based on Blockchain and Advanced Cryptographic Techniques with Byzantine Fault Tolerance (BFT) to enhance confidentiality of information in decentralized environments in response to the rising demand of safe and private transfer of information. Federated training also secures the confidentiality of users in that multiple decentralized customers collaborate to create a shared model without revealing personal data. Nevertheless, classical federated instruction has a number of safety issues, including delays in communication, model poisoning, and manipulation of the data. Based on the blockchain system, this design produces an immutable and transactive history of tracking model changes in the participating customers to ensure accountability and traceability. Offer state of the art cryptographic methods which protect the transmission of information and modeling integrity, prevent unauthorized access and modification, to further secure the instruction process. The Byzantine Fault Tolerance mechanism improves the ability of the structure to endure hostile attacks and failure of operations by solving the problem caused by evil or defective customers. As the research results indicate, the suggested design reduces the complexity of the computations that are commonly required by security-federated systems when it comes to learning and at the same time possesses a high model accuracy and resilience against adversarial actions. This structure underpins safe, effective and robust federated learning in critical applications where the quality of information and privacy of users are paramount, such as medical care, financial services as well as IoT networks.