Tackling Big Data Challenges in Large Language Models with An Emphasis on Sharing, Security, and Scalability

This paper proposes a novel approach to huge data challenges in large language models (LLMs) that prioritises sharing, security, and scale. Our solution leverages modern encryption algorithms, distributed computing tools, and edge computing principles to secure data and speed up computers. We routinely outperform previous methods in accuracy, precision, memory, and model fitting rates. Our research found that the strategy protects private data, reduces interaction costs, and speeds up model training. Cutting-edge technology allows accurate forecasts and seamless computer operations in many contexts. Ablation studies also demonstrate how our approach's components work together to improve performance. This paper advances AI-driven LLMs by demonstrating a secure, affordable, and scalable method for today's data issues. Future research may concentrate on blockchain and shared learning's larger applications and advancements as data security and computer scale become more important. Our strategy is a huge step towards creating safe and dependable AI systems that can manage today's data-driven situations.