Towards Secure and Private AI: A Framework for Decentralized Inference
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 28 Jul 2024.
Builds on
16 of 34 references.
References · 16
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
- 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
- 2024EAGLE: Speculative Sampling Requires Rethinking Feature Uncertaintycited by 578
- 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
- 2024EAGLE-2: Faster Inference of Language Models with Dynamic Draft Treescited by 370
- 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
- 2020DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environmentscited by 58
- 2024zkLLM: Zero Knowledge Proofs for Large Language Modelscited by 51
- 2022Privately Fine-Tuning Large Language Models with Differential Privacycited by 39
- 2022Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learningcited by 22
- 2023zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Trainingcited by 20
4 more
- 2023Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learningcited by 15
- 2021Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensuscited by 14
- 2022Binarizing Split Learning for Data Privacy Enhancement and Computation Reductioncited by 11
- 2022A Review of zk-SNARKscited by 8
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Citing papers per year: 2026: 1.
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