117 citations · 596 across the 24 of their papers we have counts for
36 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…
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…
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Xueping Peng, Guodong Long, Tao Shen +2
Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a pati…
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Guodong Long, Tao Shen, Yue Tan +3
Privacy protection is an ethical issue with broad concern in Artificial Intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across user…