Integration of Federated Learning (FL) with Blockchain technology to decentralized privacy-preserving, and scalable framework for strengthening cybersecurity. As cyber threats like ransomware, malware, and network intrusions grow in complexity, there is an increasing need for collaborative threat detection and mitigation. However, traditional collaborative approaches often involve sharing sensitive information across organizations, raising significant privacy concerns and regulatory challenges under frameworks like GDPR and HIPAA. FL works to solve these problems through enabling multiple entities to work together on training machine learning models without sharing their original information. Despite its advantages, FL faces challenges such as the risk of model tampering, trust deficits between participants, and dependence on a centralized server for model aggregation. To overcome these limitations the Blockchain technologies will be in used so blockchain technology provides a distributed, transparent, and non-mutable ledger that safely manages FL operations. It helps preserve the accuracy and trustworthiness of model updates via smart contracts along with consensus mechanisms, bypassing the requirement fora central aggregator. In addition, blockchain enables incentivization by introducing token-based rewards, encouraging active participation in collaborative threat detection networks. Privacy- preserving techniques to boost information security, techniques like differential privacy and homomorphic encryption are also put into practice. Such a integration of FL and blockchain is particularly impactful in securing distributed systems such as IoT devices, critical infrastructure, and enterprise networks, where privacy, trust, and scalability are crucial. This project aims to demonstrate the practical implementation of this framework, paving the way for adaptive and globally scalable cyber security systems to combat evolving threats.
Rokade et al. (Sat,) studied this question.