1 citations · 1 across the 23 of their papers we have counts for
24 papers
Pallas: A Proactive KV Cache Migration Framework for LLM Inference in AI-RAN
Tianhang Ding, Jianchun Liu, Hongli Xu
AI-RAN brings large language model (LLM) serving close to mobile users, but cellular handover can separate an active request from its inference state: the user attaches to a target…
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…
Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
Shilong Wang, Jianchun Liu, Hongli Xu +3
Decentralized Federated Graph Learning (DFGL) overcomes potential bottlenecks of the parameter server in FGL by establishing a peer-to-peer (P2P) communication network among worker…
Accelerating Mixture-of-Expert Inference with Adaptive Expert Split Mechanism
Jiaming Yan, Jianchun Liu, Hongli Xu +1
Mixture-of-Experts (MoE) has emerged as a promising architecture for modern large language models (LLMs). However, massive parameters impose heavy GPU memory (i.e., VRAM) demands,…
Adaptive KV-Cache Compression without Manually Setting Budget
Chenxia Tang, Jianchun Liu, Hongli Xu +1
Large language models (LLMs) inference relies heavily on KV-caches to accelerate autoregressive decoding, but the resulting memory footprint grows rapidly with sequence length, pos…
Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation
Qianpiao Ma, Junlong Zhou, Xiangpeng Hou +4
Federated learning (FL) is a new paradigm to train AI models over distributed edge devices (i.e., workers) using their local data, while confronting various challenges including co…
Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
Yujia Huo, Jianchun Liu, Hongli Xu +3
Federated fine-tuning (FedFT) of large language models (LLMs) has emerged as a promising solution for adapting models to distributed data environments while ensuring data privacy.…
FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning
Rukuo Li, Jianchun Liu, Hongli Xu +1
Federated fine-tuning (FedFT) provides an effective paradigm for fine-tuning large language models (LLMs) in privacy-sensitive scenarios. However, practical deployment remains chal…
Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
Shilong Wang, Jianchun Liu, Hongli Xu +2
Fine-tuning plays a crucial role in enabling pre-trained LLMs to evolve from general language comprehension to task-specific expertise. To preserve user data privacy, federated fin…
Collaborative Speculative Inference for Efficient LLM Inference Serving
Luyao Gao, Jianchun Liu, Hongli Xu +3
Speculative inference is a promising paradigm employing small speculative models (SSMs) as drafters to generate draft tokens, which are subsequently verified in parallel by the tar…
Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices
Jun Liu, Yunming Liao, Hongli Xu +3
Federated fine-tuning (FedFT) has been proposed to fine-tune the pre-trained language models in a distributed manner. However, there are two critical challenges for efficient FedFT…
A Robust Federated Learning Framework for Undependable Devices at Scale
Shilong Wang, Jianchun Liu, Hongli Xu +4
In a federated learning (FL) system, many devices, such as smartphones, are often undependable (e.g., frequently disconnected from WiFi) during training. Existing FL frameworks alw…
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning
Jiaming Yan, Jianchun Liu, Hongli Xu +4
Compression is an efficient way to relieve the tremendous communication overhead of federated learning (FL) systems. However, for the existing works, the information loss under com…
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics
Xianjun Gao, Jianchun Liu, Hongli Xu +2
Federated Graph Learning (FGL) has demonstrated the advantage of training a global Graph Neural Network (GNN) model across distributed clients using their local graph data. Unlike…
Accelerating End-Cloud Collaborative Inference via Near Bubble-free Pipeline Optimization
Luyao Gao, Jianchun Liu, Hongli Xu +3
End-cloud collaboration offers a promising strategy to enhance the Quality of Service (QoS) in DNN inference by offloading portions of the inference workload from end devices to cl…
Many Hands Make Light Work: Accelerating Edge Inference via Multi-Client Collaborative Caching
Wenyi Liang, Jianchun Liu, Hongli Xu +2
Edge inference is a technology that enables real-time data processing and analysis on clients near the data source. To ensure compliance with the Service-Level Objectives (SLOs), s…
Top-: Not All Logits Are You Need
Chenxia Tang, Jianchun Liu, Hongli Xu +1
Large language models (LLMs) typically employ greedy decoding or low-temperature sampling for reasoning tasks, reflecting a perceived trade-off between diversity and accuracy. We c…
Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs
Longwen Wang, Jianchun Liu, Zhi Liu +1
Federated Graph Learning (FGL) is an emerging technology that enables clients to collaboratively train powerful Graph Neural Networks (GNNs) in a distributed manner without exposin…
Heroes: Lightweight Federated Learning with Neural Composition and Adaptive Local Update in Heterogeneous Edge Networks
Jiaming Yan, Jianchun Liu, Shilong Wang +3
Federated Learning (FL) enables distributed clients to collaboratively train models without exposing their private data. However, it is difficult to implement efficient FL due to l…