1 citations · 1 across the 8 of their papers we have counts for
8 papers
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…