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cs.LG2026
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…
cs.LG2026
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…