5 citations · 9 across the 6 of their papers we have counts for
6 papers
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis
Yanjie Dong, Luya Wang, Yuanfang Chi +5
A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently…
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach
Naifu Zhang, Meixia Tao, Jia Wang +1
One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters. Se…
Bounds all around: training energy-based models with bidirectional bounds
Cong Geng, Jia Wang, Zhiyong Gao +2
Energy-based models (EBMs) provide an elegant framework for density estimation, but they are notoriously difficult to train. Recent work has established links to generative adversa…
Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning
Naifu Zhang, Meixia Tao, Jia Wang
One of the main focus in federated learning (FL) is the communication efficiency since a large number of participating edge devices send their updates to the edge server at each ro…
KNN-enhanced Deep Learning Against Noisy Labels
Shuyu Kong, You Li, Jia Wang +2
Supervised learning on Deep Neural Networks (DNNs) is data hungry. Optimizing performance of DNN in the presence of noisy labels has become of paramount importance since collecting…
Generative Model without Prior Distribution Matching
Cong Geng, Jia Wang, Li Chen +1
Variational Autoencoder (VAE) and its variations are classic generative models by learning a low-dimensional latent representation to satisfy some prior distribution (e.g., Gaussia…