Presentation
TrustChain AI: A Privacy-Preserving Decentralized Architecture for Large Language Model Aggregation
DescriptionThis paper introduces TrustChain AI, a decentralized architecture designed for privacy-preserving aggregation of large language models (LLMs) across multiple institutions. By integrating federated learning with knowledge distillation, TrustChain AI enables collaborative model training without the need to share raw data, addressing critical privacy concerns inherent in multi-institutional collaborations. The architecture leverages the IOTA Tangle for decentralized trust mechanisms, ensuring immutable and verifiable logging of model updates to foster transparency and trust among participants. A consensus voting mechanism ensures that only validated updates contribute to the global model, enhancing the integrity of the aggregation process. Secure communication is facilitated through the Matrix protocol, providing end-to-end encryption for all exchanges. Additionally, computational overhead is decentralized, as institutions focus on fine-tuning specific tasks while benefiting from the shared knowledge of the aggregated global model. The architecture is designed with automation in mind, enabling seamless integration and operation across diverse environments. Experimental results indicate that TrustChain AI achieves high task performance while maintaining scalability and adaptability across heterogeneous environments. This work presents a viable solution for secure, scalable, and privacy-preserving collaborative LLM development, with potential extensions to various domains requiring data integrity, confidentiality, and automated operations
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby