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Found 5 papers, 1 papers with code
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data.
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks
The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices.
Cost-aware Generalized $α$-investing for Multiple Hypothesis Testing
We extend cost-aware ERO investing to finite-horizon testing which enables the decision rule to allocate samples in a non-myopic manner.
Age-Stratified COVID-19 Spread Analysis and Vaccination: A Multitype Random Network Approach
As a result, age-stratified modeling for COVID-19 dynamics is the key to study how to reduce hospitalizations and mortality from COVID-19.
Deep-gKnock: nonlinear group-feature selection with deep neural network
To relax the linear constraint, we combine the deep neural networks (DNNs) with the recent Knockoffs technique, which has been successful in an individual feature selection context.
