ENHANCING PRIVACY AND SECURITY IN CLOUD AI: AN INTEGRATED APPROACH USING BLOCKCHAIN AND FEDERATED LEARNING
Keywords:
Blockchain-enabled Federated Learning, Privacy-preserving Cloud AI, Decentralized Model Updates, Cross-Cloud Interoperability, Distributed AI SecurityAbstract
This article explores the innovative convergence of blockchain technology, federated learning, and cloud AI to address critical challenges in privacy, security, and trust within distributed artificial intelligence systems. We present a comprehensive framework that leverages the decentralized and immutable nature of blockchain to enhance the robustness and transparency of federated learning processes deployed across multiple cloud providers. The proposed approach enables organizations to collaboratively train AI models on sensitive and distributed datasets without compromising data privacy or security. We delve into the key components of this integrated system, including decentralized model updates, smart contracts for consensus, and blockchain-based incentive mechanisms. The potential benefits, such as enhanced privacy, improved trust, and cross-cloud interoperability, are thoroughly examined. Additionally, we discuss the challenges and future research directions, including scalability issues, advanced privacy-preserving techniques, and regulatory compliance. The article also explores potential applications across various sectors, including healthcare, finance, smart cities, and supply chain management, highlighting the transformative potential of this approach in enabling secure and collaborative AI development. Through this research, we aim to contribute to the evolving landscape of privacy-preserving AI and pave the way for more inclusive and responsible artificial intelligence systems.
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