Beyond that, federated learning and blockchain technology can also be implemented in different applications like healthcare, finance, and research. Secure AI training means high model accuracy combined with regulatory compliance. The future research will focus on further streamlining and optimizing homomorphic encryption techniques and cross-chain interoperability with incentive structure refinements to bolster the decentralized AI ecosystem’s efficiency and security.
The adaptable nature of the entire framework also positions it to empower AI-driven innovations in domains that demand maximum trust, compliance, and efficiency. Lastly, as long as AI is in transformation, decentralized approaches will become even more crucial in ensuring legitimate data innovation, security, and other ethical concerns.
Sanjeev Kumar Pellikoduku has thus, put forward an innovative solution for decentralized AI training that unites federated learning with blockchain and privacy-preserving techniques. His research constitutes the basis for scaling secure and efficient AI development that stimulates innovations in privacy-preserving applications of machine learning. The further maturation of such technologies will thus prove indispensable in the development of AI through enhanced collaboration that answers pressing concerns regarding data privacy, security, and access.
