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A Joint Gradient and Loss Based Clustered Federated Learning Design
In particular, our designed clustered FL algorithm must overcome two challenges associated with FL training.
Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications
Federated Learning (FL) has revolutionized how we train deep neural networks by enabling decentralized collaboration while safeguarding sensitive data and improving model performance.
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports
Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment.
Soft Merging: A Flexible and Robust Soft Model Merging Approach for Enhanced Neural Network Performance
This merging process not only enhances the model performance by converging to a better local optimum, but also minimizes computational costs, offering an efficient and explicit learning process integrated with stochastic gradient descent.
MixNN: A design for protecting deep learning models
In this paper, we propose a novel design, called MixNN, for protecting deep learning model structure and parameters.
Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning
In addition, Byzantine faults including software, hardware, network issues occur in distributed systems which also lead to a negative impact on the prediction outcome.
Bayesian machine learning for Boltzmann machine in quantum-enhanced feature spaces
Specifically, the training phase provides a physical quantity to measure the posterior distribution in quantum feature spaces, and this measure is utilized to design the quantum maximum a posterior (QMAP) algorithm and the quantum predictive distribution estimator (QPDE).
