72 citations · 104 across the 13 of their papers we have counts for
9 papers · 1 filter
SADA: Stability-guided Adaptive Diffusion Acceleration
Ting Jiang, Yixiao Wang, Hancheng Ye +7
Diffusion models have achieved remarkable success in generative tasks but suffer from high computational costs due to their iterative sampling process and quadratic attention costs…
Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity
Yide Ran, Wentao Guo, Jingwei Sun +7
Federated Learning enables collaborative fine-tuning of Large Language Models (LLMs) across decentralized Non-Independent and Identically Distributed (Non-IID) clients, but such mo…
Keyframe-oriented Vision Token Pruning: Enhancing Efficiency of Large Vision Language Models on Long-Form Video Processing
Yudong Liu, Jingwei Sun, Yueqian Lin +6
Vision language models (VLMs) demonstrate strong capabilities in jointly processing visual and textual data. However, they often incur substantial computational overhead due to red…
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents
Yuqi Jia, Saeed Vahidian, Jingwei Sun +4
Data heterogeneity presents significant challenges for federated learning (FL). Recently, dataset distillation techniques have been introduced, and performed at the client level, t…
SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
Zhixu Du, Shiyu Li, Yuhao Wu +7
Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era of large models due to its inherent advantage, i.e., enlarging model capacity without incurring notable…
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
Jingwei Sun, Ziyue Xu, Dong Yang +6
Federated learning is a popular collaborative learning approach that enables clients to train a global model without sharing their local data. Vertical federated learning (VFL) dea…