17 citations · 40 across the 9 of their papers we have counts for
6 papers · 1 filter
Coordinating Momenta for Cross-silo Federated Learning
An Xu, Heng Huang
Communication efficiency is crucial for federated learning (FL). Conducting local training steps in clients to reduce the communication frequency between clients and the server is…
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning
Bin Gu, An Xu, Zhouyuan Huo +2
The privacy-preserving federated learning for vertically partitioned data has shown promising results as the solution of the emerging multi-party joint modeling application, in whi…
Step-Ahead Error Feedback for Distributed Training with Compressed Gradient
An Xu, Zhouyuan Huo, Heng Huang
Although the distributed machine learning methods can speed up the training of large deep neural networks, the communication cost has become the non-negligible bottleneck to constr…
Detached Error Feedback for Distributed SGD with Random Sparsification
An Xu, Heng Huang
The communication bottleneck has been a critical problem in large-scale distributed deep learning. In this work, we study distributed SGD with random block-wise sparsification as t…
Optimal Gradient Quantization Condition for Communication-Efficient Distributed Training
An Xu, Zhouyuan Huo, Heng Huang
The communication of gradients is costly for training deep neural networks with multiple devices in computer vision applications. In particular, the growing size of deep learning m…
On the Acceleration of Deep Learning Model Parallelism with Staleness
An Xu, Zhouyuan Huo, Heng Huang
Training the deep convolutional neural network for computer vision problems is slow and inefficient, especially when it is large and distributed across multiple devices. The ineffi…