# Towards Secure and Private AI: A Framework for Decentralized Inference

arXiv: https://arxiv.org/abs/2407.19401 · HTML: https://mlsanity.com/paper/2407.19401/

## Key facts

- Cited by 1 papers 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 (most cited first, 16 of 16)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty](https://mlsanity.com/paper/2401.15077/) (2024)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees](https://mlsanity.com/paper/2406.16858/) (2024)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments](https://mlsanity.com/paper/2004.05703/) (2020)
- [zkLLM: Zero Knowledge Proofs for Large Language Models](https://mlsanity.com/paper/2404.16109/) (2024)
- [Privately Fine-Tuning Large Language Models with Differential Privacy](https://mlsanity.com/paper/2210.15042/) (2022)
- [Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning](https://mlsanity.com/paper/2202.09027/) (2022)
- [zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training](https://mlsanity.com/paper/2307.16273/) (2023)
- [Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning](https://mlsanity.com/paper/2301.08778/) (2023)
- [Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus](https://mlsanity.com/paper/2101.03300/) (2021)
- [Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction](https://mlsanity.com/paper/2206.04864/) (2022)
- [A Review of zk-SNARKs](https://mlsanity.com/paper/2202.06877/) (2022)

## Built on by (most cited first, 1 of 1)

- [The Rise of Sparse Mixture-of-Experts: A Survey from Algorithmic Foundations to Decentralized Architectures and Vertical Domain Applications](https://mlsanity.com/paper/2602.08019/) (2026)

## 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.