8 citations · 12 across the 15 of their papers we have counts for
4 papers · 1 filter
Invariant Federated Learning for Edge Intelligence: Mitigating Heterogeneity and Asynchrony via Exit Strategy and Invariant Penalty
Ziruo Hao, Zhenhua Cui, Tao Yang +3
This paper provides an invariant federated learning system for resource-constrained edge intelligence. This framework can mitigate the impact of heterogeneity and asynchrony via ex…
The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence
Ziruo Hao, Zhenhua Cui, Tao Yang +3
Federated learning (FL) has provided a new methodology for coordinating a group of clients to train a machine learning model collaboratively, bringing an efficient paradigm in edge…
The Transferability of Downsamped Sparse Graph Convolutional Networks
Qinji Shu, Hang Sheng, Feng Ji +2
To accelerate the training of graph convolutional networks (GCNs) on real-world large-scale sparse graphs, downsampling methods are commonly employed as a preprocessing step. Howev…
Regularized Recovery by Multi-order Partial Hypergraph Total Variation
Ruyuan Qu, Jiaqi He, Hui Feng +2
Capturing complex high-order interactions among data is an important task in many scenarios. A common way to model high-order interactions is to use hypergraphs whose topology can…