195 citations · 1.2k across the 104 of their papers we have counts for
7 papers · 1 filter
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…
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…
BAQ: Efficient Bit Allocation Quantization for Large Language Models
Chao Zhang, Li Wang, Samson Lasaulce +1
Post-training model quantization is a widely adopted technique for reducing the memory and computational costs of large language models (LLMs). However, most existing methods rely…
Strategic Federated Learning: Application to Smart Meter Data Clustering
Hassan Mohamad, Chao Zhang, Samson Lasaulce +3
Federated learning (FL) involves several clients that share with a fusion center (FC), the model each client has trained with its own data. Conventional FL, which can be interprete…
Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling
Chao Zhang, Samson Lasaulce, Martin Hennebel +3
Data clustering is an instrumental tool in the area of energy resource management. One problem with conventional clustering is that it does not take the final use of the clustered…
Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation
Hang Zou, Chao Zhang, Samson Lasaulce +2
In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended de…