research
∙
09/04/2023
Federated cINN Clustering for Accurate Clustered Federated Learning
Federated Learning (FL) presents an innovative approach to privacy-prese...
share
research
∙
08/31/2023
Amplitude Prediction from Uplink to Downlink CSI against Receiver Distortion in FDD Systems
In frequency division duplex (FDD) massive multiple-input multiple-outpu...
research
∙
02/27/2023
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence
Reducing communication overhead in federated learning (FL) is challengin...
research
∙
11/19/2022
Personalized Federated Learning with Hidden Information on Personalized Prior
Federated learning (FL for simplification) is a distributed machine lear...
research
∙
04/06/2022
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg
Federated learning (FL) is identified as a crucial enabler for large-sca...
research
∙
03/13/2022
Deep Learning for 1-Bit Compressed Sensing-based Superimposed CSI Feedback
In frequency-division duplexing (FDD) massive multiple-input multiple-ou...
research
∙
05/03/2021
MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price
Bitcoin, one of the major cryptocurrencies, presents great opportunities...
research
∙
05/03/2021
Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search
Arrhythmia is a cardiovascular disease that manifests irregular heartbea...
research
∙
04/21/2021
LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences
In educational applications, Knowledge Tracing (KT), the problem of accu...
research
∙
12/22/2020
Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithms
As an important problem in modern data analytics, classification has wit...
research
∙
09/06/2020
HLSGD Hierarchical Local SGD With Stale Gradients Featuring
While distributed training significantly speeds up the training process ...
research
∙
09/06/2020
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks
Parameter updating is an important stage in parallelism-based distribute...
research
∙
07/23/2020
DBS: Dynamic Batch Size For Distributed Deep Neural Network Training
Synchronous strategies with data parallelism, such as the Synchronous St...
research
∙
06/18/2019
Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors
Cardiac segmentation from late gadolinium enhancement MRI is an importan...
research
∙
09/03/2018