195 citations · 1.2k across the 104 of their papers we have counts for
108 papers
FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization
Hang Zou, Chao Zhang, Yuzhi Yang +3
Federated Learning (FL) enables privacy-preserving fine-tuning of Large Language Models (LLMs), yet the massive communication overhead remains a critical bottleneck. Furthermore, a…
Channel Gains to Captions: Task-Unified Multi-Level RF Sensing with Vision-Language Models
Tianyu Hu, Zhiren Gong, Haowei Cui +6
This letter investigates a task-unified multi-level radio-frequency (RF) sensing framework driven by vision-language models (VLMs). Existing RF sensing methods rely on task-specifi…
Network-Induced Strategic Communication in Opinion Dynamics
Hassan Munif, Anthony Couthures, Vineeth S. Varma +2
Classical opinion dynamics typically assume a fixed mapping from private opinions to public signals, such as linear exchange, saturated signaling, or discrete public actions. In th…
Social learning community detection with nonlinear interaction
Anthony Couthures, Athira Varma Jayakumar, Vineeth Satheeskumar Varma +3
Conventional community detection requires centralized network data, making it unsuitable for distributed or privacy-preserving systems. In this paper, we demonstrate that macroscop…
ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression
Wenya Yu, Chao Zhang, Li Wang +2
Post-Training Quantization (PTQ) and Low-Rank Adaptation (LoRA) constitute the standard pipeline for efficient Large Language Model (LLM) deployment. However, applying them sequent…
Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
Hang Zou, Yuzhi Yang, Lina Bariah +15
The integration of machine learning tools into telecom networks, has led to two prevailing paradigms, namely, language-based systems, such as Large Language Models (LLMs), and phys…