1 citations · 1 across the 5 of their papers we have counts for
5 papers
Modular Foundation Model Inference at the Edge: Network-Aware Microservice Optimization
Juan Zhu, Zixin Wang, Shenghui Song +2
Foundation models (FMs) unlock unprecedented multimodal and multitask intelligence, yet their cloud-centric deployment precludes real-time responsiveness and compromises user priva…
Edge Large AI Models: Collaborative Deployment and IoT Applications
Zixin Wang, Yuanming Shi, Khaled. B. Letaief
Large artificial intelligence models (LAMs) emulate human-like problem-solving capabilities across diverse domains, modalities, and tasks. By leveraging the communication and compu…
Edge Large AI Models: Revolutionizing 6G Networks
Zixin Wang, Yuanming Shi, Yong Zhou +2
Large artificial intelligence models (LAMs) possess human-like abilities to solve a wide range of real-world problems, exemplifying the potential of experts in various domains and…
Mutual Information-Empowered Task-Oriented Communication: Principles, Applications and Challenges
Hongru Li, Songjie Xie, Jiawei Shao +5
Mutual information (MI)-based guidelines have recently proven to be effective for designing task-oriented communication systems, where the ultimate goal is to extract and transmit…
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks
Tianqu Kang, Zixin Wang, Hengtao He +3
Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigate…