117 citations · 626 across the 33 of their papers we have counts for
51 papers
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Yue Tan, Yixin Liu, Guodong Long +3
Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing to the advantages of federated learning, federated graph learning (FGL) enables clients to t…
Unsupervised Knowledge Graph Construction and Event-centric Knowledge Infusion for Scientific NLI
Chenglin Wang, Yucheng Zhou, Guodong Long +2
With the advance of natural language inference (NLI), a rising demand for NLI is to handle scientific texts. Existing methods depend on pre-trained models (PTM) which lack domain-s…
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
Yue Tan, Guodong Long, Jie Ma +3
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to learn collaboratively without sharing their private data. However, excessive computation…
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels
Zhuowei Wang, Tianyi Zhou, Guodong Long +2
Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are…
Personalized Federated Learning With Graph
Fengwen Chen, Guodong Long, Zonghan Wu +2
Knowledge sharing and model personalization are two key components in the conceptual framework of personalized federated learning (PFL). Existing PFL methods focus on proposing new…
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification
Yucheng Zhou, Tao Shen, Xiubo Geng +2
Generating new events given context with correlated ones plays a crucial role in many event-centric reasoning tasks. Existing works either limit their scope to specific scenarios o…