7 papers
Versatile yet Efficient Network Traffic Analysis: Offloading Network Foundation Model to SmartNIC
Chungang Lin, Xuying Meng, Tianyu Zuo +9
Pervasive encryption makes large-scale labeling infeasible for traffic analysis, while security operations demand edge analysis to avert service degradation and further vulnerabili…
RPO: Fine-Tuning Visual Generative Models via Rich Vision-Language Preferences
Hanyang Zhao, Haoxian Chen, Yucheng Guo +5
Traditional preference tuning methods for LLMs/Visual Generative Models often rely solely on reward model labeling, which can be opaque, offer limited insights into the rationale b…
VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo
Qianli Ma, Yaowei Zheng, Zhelun Shi +9
Recent advances in large language models (LLMs) have driven impressive progress in omni-modal understanding and generation. However, training omni-modal LLMs remains a significant…
Distillation-Enhanced Clustering Acceleration for Encrypted Traffic Classification
Ziyue Huang, Chungang Lin, Weiyao Zhang +2
Traffic classification plays a significant role in network service management. The advancement of deep learning has established pretrained models as a robust approach for this task…
Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation
Yukun Jiang, Peiran Wang, Chengguo Lin +2
Two-party split learning has emerged as a popular paradigm for vertical federated learning. To preserve the privacy of the label owner, split learning utilizes a split model, which…
BeamVQ: Aligning Space-Time Forecasting Model via Self-training on Physics-aware Metrics
Hao Wu, Xingjian Shi, Ziyue Huang +6
Data-driven deep learning has emerged as the new paradigm to model complex physical space-time systems. These data-driven methods learn patterns by optimizing statistical metrics a…