3 citations · 4 across the 10 of their papers we have counts for
11 papers
FLoKD: Adaptive Knowledge Distillation for Federated Low-Rank LLM over Wireless Networks
Xinlu Zhang, Na Yan, Yang Su +2
Large language models (LLMs) have demonstrated strong capabilities across a wide range of natural language processing tasks. However, conventional fine-tuning typically relies on c…
GRACE: Generative Recommender Acceleration Engine for Real-Time Ads Retrieval
Zhou Fang, Yuhang Huang, Ang Zhang +12
Productionizing generative recommenders for high-volume, real-time ads retrieval creates two serving challenges: eligibility, ensuring that each generated ad is eligible for the re…
Experience Graphs: The Data Foundation for Self-Improving Agents
Gang Liao, Yujia He, Abdullah Ozturk +22
The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures. We argue that long-horizon agentic tasks -- c…
CoLLM: Continuous Adaptation for SLO-Aware LLM Serving on Shared GPU Clusters
Shaoyuan Huang, Yunfeng Zhao, Na Yan +5
As Large Language Models (LLMs) are increasingly adopted in edge intelligence to power domain-specific applications and personalized services, the quality and efficiency of the LLM…
KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
Gang Liao, Hongsen Qin, Ying Wang +36
Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture div…
FairMT: Fairness for Heterogeneous Multi-Task Learning
Guanyu Hu, Tangzheng Lian, Na Yan +5
Fairness in machine learning has been extensively studied in single-task settings, while fair multi-task learning (MTL), especially with heterogeneous tasks (classification, detect…
Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks
Xinlu Zhang, Na Yan, Yang Su +2
Federated learning (FL) for large language models (LLMs) offers a privacy-preserving scheme, enabling clients to collaboratively fine-tune locally deployed LLMs or smaller language…
PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts
Yang Su, Na Yan, Yansha Deng +1
Large language models (LLMs) hosted on cloud servers alleviate the computational and storage burdens on local devices but raise privacy concerns due to sensitive data transmission…
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi +132
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions…
Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions
Na Yan, Yang Su, Yansha Deng +1
Federated learning (FL) provides a privacy-preserving solution for fine-tuning pre-trained large language models (LLMs) using distributed private datasets, enabling task-specific a…
HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization
Yang Su, Na Yan, Yansha Deng +2
Federated fine-tuning of pre-trained Large Language Models (LLMs) enables task-specific adaptation across diverse datasets while preserving privacy. However, challenges such as hig…