20 papers
Scaling Synthetic-Image Pre-Training for Federated Fine-Tuning of Large Vision Models
Qianpiao Ma, Xiaozhu Song, Junlong Zhou +3
Federated fine-tuning (FedFT) enables adapting pre-trained large vision models (LVMs) on distributed, privacy-sensitive devices, while its practical deployment is hindered by three…
Bandwidth-Aware and Cost-Efficient Pipeline Parallel Scheduling in Geo-Distributed LLM Training
Han Zhang, Jianchun Liu, Hongli Xu
The rapid evolution of large language models (LLMs) has made geographically distributed training necessary due to GPU scarcity within a single cloud region. In such cross-region se…
Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning
Weihang Li, Jianchun Liu, Hongli Xu
LoRA-MoE has emerged as an effective paradigm for parameter-efficient fine-tuning, combining the low training cost of LoRA with the increased adaptation capacity of Mixture-of-Expe…
Beyond Physical Labels: Redefining Domains for Robust WiFi-based Gesture Recognition
Xiang Zhang, Huan Yan, Jinyang Huang +6
In this paper, we propose GesFi, a novel WiFi-based gesture recognition system that introduces WiFi latent domain mining to redefine domains directly from the data itself. GesFi fi…
Improving LLM Reasoning via Dependency-Aware Query Decomposition and Logic-Parallel Content Expansion
Xianjun Gao, Jianchun Liu, Hongli Xu +1
The integration of Large Language Models (LLMs) into real-time Web applications, such as AI-powered search and conversational agents, presents a fundamental Web infrastructure chal…
SABlock: Semantic-Aware KV Cache Eviction with Adaptive Compression Block Size
Jinhan Chen, Jianchun Liu, Hongli Xu +2
The growing memory footprint of the Key-Value (KV) cache poses a severe scalability bottleneck for long-context Large Language Model (LLM) inference. While KV cache eviction has em…