63 citations · 91 across the 5 of their papers we have counts for
6 papers
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels
Qiuchen Zhang, Jing Ma, Jian Lou +2
Deep learning models trained on large-scale data have achieved encouraging performance in many real-world tasks. Meanwhile, publishing those models trained on sensitive datasets, s…
Temporal Network Embedding via Tensor Factorization
Jing Ma, Qiuchen Zhang, Jian Lou +2
Representation learning on static graph-structured data has shown a significant impact on many real-world applications. However, less attention has been paid to the evolving nature…
SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling
Haowen Lin, Jian Lou, Li Xiong +1
Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy. Ho…
RobustFed: A Truth Inference Approach for Robust Federated Learning
Farnaz Tahmasebian, Jian Lou, Li Xiong
Federated learning is a prominent framework that enables clients (e.g., mobile devices or organizations) to train a collaboratively global model under a central server's orchestrat…
Federated Graph Classification over Non-IID Graphs
Han Xie, Jing Ma, Li Xiong +1
Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can al…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…