The rapid advancement of ML models in critical sectors such as healthcare, finance, and security has intensified the need for robust data security, model integrity, and reliable outputs. Large multimodal foundational models, while crucial for complex tasks, present challenges in scalability, reliability, and potential misuse. Decentralized systems offer a solution by distributing workload and mitigating central points of failure, but they introduce risks of unauthorized access to sensitive data across nodes. We address these challenges with a comprehensive framework designed for responsible AI development. Our approach incorporates: 1) Zero-knowledge proofs for secure model verification, enhancing trust without compromising privacy. 2) Consensus-based verification checks to ensure consistent outputs across nodes, mitigating hallucinations and maintaining model integrity. 3) Split Learning techniques that segment models across different nodes, preserving data privacy by preventing full data access at any point. 4) Hardware-based security through trusted execution environments (TEEs) to protect data and computations. This framework aims to enhance security and privacy and improve the reliability and fairness of multimodal AI systems. Promoting efficient resource utilization contributes to more sustainable AI development. Our state-of-the-art proofs and principles demonstrate the framework's effectiveness in responsibly democratizing artificial intelligence, offering a promising approach for building secure and private foundational models.

翻译：机器学习模型在医疗、金融和安全等关键领域的快速发展，加强了对数据安全、模型完整性和输出可靠性的迫切需求。大型多模态基础模型虽然在复杂任务中至关重要，但在可扩展性、可靠性和潜在滥用方面存在挑战。去中心化系统通过分配工作负载和减少单点故障提供了解决方案，但也带来了节点间敏感数据未经授权访问的风险。我们通过一个为负责任的人工智能开发设计的综合框架应对这些挑战。我们的方法整合了：1）用于安全模型验证的零知识证明，在不损害隐私的前提下增强信任；2）基于共识的验证检查，确保节点间输出的一致性，减少幻觉并保持模型完整性；3）分割学习技术，将模型分段部署于不同节点，通过防止任何节点访问完整数据来保护数据隐私；4）通过可信执行环境（TEEs）实现基于硬件的安全保护，保障数据和计算安全。该框架旨在增强安全性和隐私性，并提升多模态人工智能系统的可靠性与公平性。通过促进高效的资源利用，它有助于实现更可持续的人工智能发展。我们最先进的证明和原理展示了该框架在负责任地普及人工智能方面的有效性，为构建安全且隐私保护的基础模型提供了一种前景广阔的方法。