13 citations · 43 across the 14 of their papers we have counts for
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cs.LG2023
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization
Yue Niu, Zalan Fabian, Sunwoo Lee +2
Quasi-Newton methods still face significant challenges in training large-scale neural networks due to additional compute costs in the Hessian related computations and instability i…
cs.LG2023★ 3 cited
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training
Tuo Zhang, Lei Gao, Sunwoo Lee +2
In cross-device Federated Learning (FL) environments, scaling synchronous FL methods is challenging as stragglers hinder the training process. Moreover, the availability of each cl…
cs.LG2023★ 3 cited
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training
Zhenheng Tang, Xiaowen Chu, Ryan Yide Ran +7
Federated Learning (FL) enables collaborations among clients for train machine learning models while protecting their data privacy. Existing FL simulation platforms that are design…