1.2k citations · 3.4k across the 50 of their papers we have counts for
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DONE: Distributed Approximate Newton-type Method for Federated Edge Learning
Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen +4
There is growing interest in applying distributed machine learning to edge computing, forming federated edge learning. Federated edge learning faces non-i.i.d. and heterogeneous da…
Stochastic Client Selection for Federated Learning with Volatile Clients
Tiansheng Huang, Weiwei Lin, Li Shen +2
Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public. In each round of synchronous FL training, only a…
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
Tiansheng Huang, Weiwei Lin, Wentai Wu +3
The issue of potential privacy leakage during centralized AI's model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, ter…
Federated Learning with Nesterov Accelerated Gradient
Zhengjie Yang, Wei Bao, Dong Yuan +2
Federated learning (FL) is a fast-developing technique that allows multiple workers to train a global model based on a distributed dataset. Conventional FL (FedAvg) employs gradien…
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
Canh T. Dinh, Nguyen H. Tran, Minh N. H. Nguyen +4
There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs)…