375 citations · 2.2k across the 69 of their papers we have counts for
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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…
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification
Yang Li, Canran Xu, Guodong Long +3
Recently, prefix-tuning was proposed to efficiently adapt pre-trained language models to a broad spectrum of natural language classification tasks. It leverages soft prefix as task…
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…
On the Convergence of Clustered Federated Learning
Jie Ma, Guodong Long, Tianyi Zhou +2
Knowledge sharing and model personalization are essential components to tackle the non-IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1)…