40 citations · 84 across the 11 of their papers we have counts for
4 papers · 1 filter
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers
Zheng Chai, Yujing Chen, Ali Anwar +3
Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized. This form of collaborative learning exposes new trade…
Tunable Subnetwork Splitting for Model-parallelism of Neural Network Training
Junxiang Wang, Zheng Chai, Yue Cheng +1
Alternating minimization methods have recently been proposed as alternatives to the gradient descent for deep neural network optimization. Alternating minimization methods can typi…
Toward Model Parallelism for Deep Neural Network based on Gradient-free ADMM Framework
Junxiang Wang, Zheng Chai, Yue Cheng +1
Alternating Direction Method of Multipliers (ADMM) has recently been proposed as a potential alternative optimizer to the Stochastic Gradient Descent(SGD) for deep learning problem…
TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad +7
Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that ex…