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09/20/2023
Preconditioned Federated Learning
Federated Learning (FL) is a distributed machine learning approach that ...
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07/21/2023
MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification
Quantum computing has shown considerable promise for compute-intensive t...
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02/03/2023
Vertical Federated Learning: Taxonomies, Threats, and Prospects
Federated learning (FL) is the most popular distributed machine learning...
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08/04/2022
Scalable Quantum Neural Networks for Classification
Many recent machine learning tasks resort to quantum computing to improv...
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07/19/2022
MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning
Federated Learning (FL), a distributed machine learning paradigm, has be...
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05/05/2022
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks
Variational quantum algorithms (VQAs) have recently received significant...
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04/22/2022
Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation
A high-resolution network exhibits remarkable capability in extracting m...
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04/04/2022
Towards Privacy-Preserving and Verifiable Federated Matrix Factorization
Recent years have witnessed the rapid growth of federated learning (FL),...
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10/26/2021
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in Machine Learning
Training high-performing deep learning models require a rich amount of d...
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06/16/2021
QuantumFed: A Federated Learning Framework for Collaborative Quantum Training
With the fast development of quantum computing and deep learning, quantu...
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05/24/2013
