1 citations · 1 across the 7 of their papers we have counts for
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Representation-Aligned Multi-Scale Personalization for Federated Learning
Wenfei Liang, Wee Peng Tay
In federated learning (FL), accommodating clients with diverse resource constraints remains a significant challenge. A widely adopted approach is to use a shared full-size model, f…
Personalized Subgraph Federated Learning with Sheaf Collaboration
Wenfei Liang, Yanan Zhao, Rui She +2
Graph-structured data is prevalent in many applications. In subgraph federated learning (FL), this data is distributed across clients, each with a local subgraph. Personalized subg…
Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
Yanan Zhao, Feng Ji, Kai Zhao +6
Graph Contrastive Learning (GCL) has recently made progress as an unsupervised graph representation learning paradigm. GCL approaches can be categorized into augmentation-based and…
Distributed-Order Fractional Graph Operating Network
Kai Zhao, Xuhao Li, Qiyu Kang +5
We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fraction…
FedSheafHN: Personalized Federated Learning on Graph-structured Data
Wenfei Liang, Yanan Zhao, Rui She +2
Personalized subgraph Federated Learning (FL) is a task that customizes Graph Neural Networks (GNNs) to individual client needs, accommodating diverse data distributions. However,…
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
Qiyu Kang, Kai Zhao, Qinxu Ding +5
We introduce the FRactional-Order graph Neural Dynamical network (FROND), a new continuous graph neural network (GNN) framework. Unlike traditional continuous GNNs that rely on int…