1 citations · 1 across the 23 of their papers we have counts for
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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…
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…
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…
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…
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…
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…
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…