Event-Driven Full-Stack Applications with Kafka and WebSockets
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I1P104Keywords:
Blockchain, Multi-Party Machine Learning (Mpml), Cloud-Edge Collaboration, Federated Learning, Secure Multi-Party Computation (Smpc), Data Privacy, Model IntegrityAbstract
The swift cloud and edge computing development promoted the implementation of distributed learning models as different organizations can train them cooperatively and share sensitive data without the need to access it. There is however a serious challenge of making sure that the data privacy, model integrity and secure collaboration is ensured. The paper introduces a blockchain-enhanced framework to conduct computations based on secure multi-party machine learning (MPML) over cloud-edge collaborative settings. The framework proposed uses blockchain technology to build a sense of trust, immutability and verifiability during data sharing as well as training the model. The framework also includes the principles of secure multi-party computation (SMPC) and federation of learning, designed to maintain the privacy of data, but to make models optimize the performance on a heterogeneous set of nodes. We also give a step by step methodology of the system architecture, consensus protocols, encryption mechanisms, and collaborative learning algorithms. Experimental testing illustrates the effectiveness of the framework in regard to security, scale, and the accuracy of the model. Indeed, our findings reveal that blockchain coupled with MPML will help to solve security threats, accountability, and trust among cooperating entities substantially. The framework offers the solid solution to the real-world cloud-edge collaborative applications, such as healthcare, finance, and smart cities
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