Blockchain-Enhanced Federated Learning: A New Paradigm for Secure Distributed Machine Learning

Merging blockchain technology into Federated Learning makes a novel approach to boost security, trust, and scalability, which are critical questions about the formation of distributed machine learning. This given industry study examines which obstacles are and possible ways of mitigating them to implement blockchain technology in such settings and attempts to solve the primary weakness of distributed learning exhibited. This is supported by our study concerning the experimental approach with an emphasis on security policy changes indicated by our results. We were able to find the risk sources $25 \%$ up, and we were able to prevent the everyday common data leaks and model poisoning risks. Also, the jump from 70 to 95 security scores shows that providers have significantly improved their cybersecurity. The integration process, however, brings along a computational overhead, which can be estimated from the $\mathbf{9 0 \%}$ performance reduction from which the system efficiency score alters from 90 to 75. The designed framework increases scaling-level issues and trust among people groups. Scalability ratings have been improved from 80 to 85. Trust metrics, however, are now $30 \%$ higher, which took the metrics from 60 to 90. These statistical data show clearly that in the pursuit of security and trust, tradeoffs may arise in the form of increased computational resources. The tut gives a good reason for using blockchain technology in the FL model. It provides a comprehensive description of both pros and cons. It proposes principles that can be followed in subsequent research to advance and strengthen the system’s resilience in conjunction with its operation. The research that is covered in this paper contributes a new framework to the volatile subject of safe and scalable machine learning. This framework is designed to elevate the trustworthiness and reliability of distributed learning systems.