34 citations · 37 across the 10 of their papers we have counts for
13 papers
Split Conformal Interval Length under Huber Contamination
Xiao Zhang, Bowen Zhang, Lie He
Existing analyses of interval length in split conformal prediction largely focus on clean data. However, how contamination at the training and calibration stages alters interval le…
Protecting Shared Agent Workflows from Invalidation Attacks with Signed Trajectories and Runtime Verification
Ruixiao Li, Fahao Chen, Peng Li
Large language model (LLM)-based agents increasingly rely on shared workflow repositories to transfer experience across tasks and agents. By abstracting past executions into reusab…
SparLeak: Privacy Leakage from Sparse Attention in LLM Inference on Shared GPUs
Fahao Chen, Linkang Du, Jinhao Zhou +2
Sparse attention is widely used to accelerate long-context inference in modern large language models (LLMs), but its input-dependent execution behavior introduces previously unexpl…
Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement
Qianli Liu, Kaibin Guo, Zicong Hong +5
Expert parallelism has become the prevailing paradigm to serve Mixture-of-Experts (MoE) models. Its efficiency depends on the communication and computation latencies of the GPUs, w…
GraphFlow: A Graph-Based Workflow Management for Efficient LLM-Agent Serving
Ao Li, Shangpeng Yang, Fahao Chen +3
Large Language Model (LLM)-based agents demonstrate strong reasoning and execution capabilities on complex tasks when guided by structured instructions, commonly referred to as wor…
Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices
Fahao Chen, Jie Wan, Peng Li +2
Federated fine-tuning of Mixture-of-Experts (MoE)-based large language models (LLMs) is challenging due to their massive computational requirements and the resource constraints of…
Semi-Clairvoyant Scheduling of Speculative Decoding Requests to Minimize LLM Inference Latency
Ruixiao Li, Fahao Chen, Peng Li
Speculative decoding accelerates Large Language Model (LLM) inference by employing a small speculative model (SSM) to generate multiple candidate tokens and verify them using the L…
Internet of Agents: Fundamentals, Applications, and Challenges
Yuntao Wang, Shaolong Guo, Yanghe Pan +6
With the rapid proliferation of large language models and vision-language models, AI agents have evolved from isolated, task-specific systems into autonomous, interactive entities…
SPIN: Accelerating Large Language Model Inference with Heterogeneous Speculative Models
Fahao Chen, Peng Li, Tom H. Luan +2
Speculative decoding has been shown as an effective way to accelerate Large Language Model (LLM) inference by using a Small Speculative Model (SSM) to generate candidate tokens in…
Mell: Memory-Efficient Large Language Model Serving via Multi-GPU KV Cache Management
Liu Qianli, Hong Zicong, Chen Fahao +2
Serving large language models (LLMs) for massive users is challenged by the significant memory footprint of the transient state, known as the key-value (KV) cache, which scales wit…
Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation
Fahao Chen, Peng Li, Zicong Hong +2
Mixture-of-Experts (MoE) is an emerging technique for scaling large models with sparse activation. MoE models are typically trained in a distributed manner with an expert paralleli…
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks
Fahao Chen, Peng Li, Celimuge Wu
Dynamic Graph Neural Network (DGNN) has shown a strong capability of learning dynamic graphs by exploiting both spatial and temporal features. Although DGNN has recently received c…
FedGraph: Federated Graph Learning with Intelligent Sampling
Fahao Chen, Peng Li, Toshiaki Miyazaki +1
Federated learning has attracted much research attention due to its privacy protection in distributed machine learning. However, existing work of federated learning mainly focuses…