200 citations · 565 across the 8 of their papers we have counts for
Showing 2021Show all
3 papers · 1 filter
cs.LG2021★ 116 cited
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
Moming Duan, Duo Liu, Xinyuan Ji +4
Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike th…
cs.LG2021★ 49 cited
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
Yu Zhang, Moming Duan, Duo Liu +5
Federated learning (FL) is an emerging distributed machine learning paradigm that protects privacy and tackles the problem of isolated data islands. At present, there are two main…
cs.LG2021★ 67 cited
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
Li Li, Moming Duan, Duo Liu +5
Federated Learning (FL) is a novel distributed machine learning which allows thousands of edge devices to train model locally without uploading data concentrically to the server. B…