DisLLM: Distributed LLMs for Privacy Assurance in Resource-Constrained Environments

Описание: 🛡 Demo: DisLLM - Distributed LLMs for Privacy Assurance in Resource-Constrained Environments
This demonstration presents DisLLM, a novel distributed learning framework that enables privacy-preserving and resource-efficient fine-tuning of Large Language Models (LLMs). By combining Federated Learning (FL), Split Learning (SL), and Low-Rank Adaptation (LoRA), DisLLM ensures that sensitive data remains on client devices while distributing computational load, making LLMs practical for resource-constrained environments.

🔑 Key Features:
Privacy-preserving distributed fine-tuning: Uses Splitfed Learning (SFL) to keep raw data on local devices while enabling collaborative model training.
Lightweight adaptation for resource efficiency: Integrates LoRA fine-tuning to reduce computational overhead while maintaining accuracy.
Enhanced security with Local Differential Privacy (LDP): Adds controlled noise to data before transmission, ensuring privacy protection.

💡 Why It Matters?
LLMs require massive datasets and computational resources, making them difficult to deploy in privacy-sensitive domains like healthcare and finance. Traditional cloud-based fine-tuning exposes sensitive data to third parties, raising security concerns. DisLLM eliminates the need to share raw data, optimizing model performance while ensuring privacy, scalability, and efficiency in low-resource settings.

✨ Technologies Used:
Splitfed Learning (SFL) for secure, hierarchical model training.
LoRA fine-tuning is used to reduce memory and computational costs.
Local Differential Privacy (LDP) for secure and noise-protected model updates.

📊 Results:
Achieves comparable accuracy to centralized fine-tuning while preserving privacy.
Reduces GPU memory usage by up to 20% compared to traditional FL architectures.
Scales efficiently across multiple clients with minimal computational overhead.

🚀 Experience the next step in privacy-preserving AI with DisLLM!

#LLM #FederatedLearning #PrivacyPreservingAI #MachineLearning #DisLLM #Netslabs