14 citations · 50 across the 7 of their papers we have counts for
10 papers · 1 filter
CADA: Communication-Adaptive Distributed Adam
Tianyi Chen, Ziye Guo, Yuejiao Sun +1
Stochastic gradient descent (SGD) has taken the stage as the primary workhorse for large-scale machine learning. It is often used with its adaptive variants such as AdaGrad, Adam,…
Hybrid Federated Learning: Algorithms and Implementation
Xinwei Zhang, Wotao Yin, Mingyi Hong +1
Federated learning (FL) is a recently proposed distributed machine learning paradigm dealing with distributed and private data sets. Based on the data partition pattern, FL is ofte…
Neural Network Compression Via Sparse Optimization
Tianyi Chen, Bo Ji, Yixin Shi +4
The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications. There have…
VAFL: a Method of Vertical Asynchronous Federated Learning
Tianyi Chen, Xiao Jin, Yuejiao Sun +1
Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from diffe…
Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets
Yanjie Dong, Georgios B. Giannakis, Tianyi Chen +3
This work investigates fault-resilient federated learning when the data samples are non-uniformly distributed across workers, and the number of faulty workers is unknown to the cen…
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
Jun Sun, Tianyi Chen, Georgios B. Giannakis +1
The present paper develops a novel aggregated gradient approach for distributed machine learning that adaptively compresses the gradient communication. The key idea is to first qua…