Wei-Jia Jia

PI-SAFE: Practical Privacy-Preserving LLM Inference With Adversarial Fine-Tuning for Optimized Utility

Cloud-based Large Language Model (LLM) inference services typically require users to submit plain-text inputs, thereby posing severe privacy risks. Existing privacy-preserving paradigms are mostly task-specific and often necessitate pervasive modifications to the entire server-side model. This reliance introduces subst...

Efficient LLM Coserving at the Edge via Resource-Aware Cooperative Scheduling

Large language models (LLMs) are increasingly deployed on edge nodes to support edge intelligence applications. To overcome limited GPU memory, offloading-based methods partition model parameters between the GPU and host memory, enabling inference on commodity hardware. However, deploying a single model instance using...

Diffusion-Guided Multi-Objective Reinforcement Learning for Hybrid Action Optimization in Collaborative Edge AIGC Networks

Adaptive AI Agent Migration via Generative Diffusion-based Reinforcement Learning in Edge Intelligence Systems

This work introduces a diffusion model as a generative prior to produce high-quality global deployment plans, effectively avoiding the local-optima problem common in conventional reinforcement learning.

Efficient Layer-Granularity Unloading for LLMs in Edge Computing

Advancements in edge computing and container technology have made it increasingly popular and convenient to deploy Large Language Models (LLMs) through containers at the edge. However, the limited GPU resources of edge servers make it impractical to retain the model in GPU memory for long periods due to the high memory...