1 citations · 3 across the 6 of their papers we have counts for
6 papers
Hypernetworks for Model-Heterogeneous Personalized Federated Learning
Chen Zhang, Husheng Li, Xiang Liu +2
Recent advances in personalized federated learning have focused on addressing client model heterogeneity. However, most existing methods still require external data, rely on model…
Yotta: A Large-Scale Trustless Data Trading Scheme for Blockchain System
Xiang Liu, Zhanpeng Guo, Liangxi Liu +3
Data trading is one of the key focuses of Web 3.0. However, all the current methods that rely on blockchain-based smart contracts for data exchange cannot support large-scale data…
One-shot Federated Learning Methods: A Practical Guide
Xiang Liu, Zhenheng Tang, Xia Li +6
One-shot Federated Learning (OFL) is a distributed machine learning paradigm that constrains client-server communication to a single round, addressing privacy and communication ove…
Enhanced Structured Lasso Pruning with Class-wise Information
Xiang Liu, Mingchen Li, Xia Li +7
Modern applications require lightweight neural network models. Most existing neural network pruning methods focus on removing unimportant filters; however, these may result in the…
Efficient Partitioning Vision Transformer on Edge Devices for Distributed Inference
Xiang Liu, Yijun Song, Xia Li +5
Deep learning models are increasingly utilized on resource-constrained edge devices for real-time data analytics. Recently, Vision Transformer and their variants have shown excepti…
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
Xiang Liu, Liangxi Liu, Feiyang Ye +4
Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning. Recently, motivated by dimini…