5 citations · 13 across the 9 of their papers we have counts for
9 papers
Preconditioned Federated Learning
Zeyi Tao, Jindi Wu, Qun Li
Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner. The standard optimization m…
MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification
Jindi Wu, Tianjie Hu, Qun Li
Quantum computing has shown considerable promise for compute-intensive tasks in recent years. For instance, classification tasks based on quantum neural networks (QNN) have garnere…
Vertical Federated Learning: Taxonomies, Threats, and Prospects
Qun Li, Chandra Thapa, Lawrence Ong +5
Federated learning (FL) is the most popular distributed machine learning technique. FL allows machine-learning models to be trained without acquiring raw data to a single point for…
Scalable Quantum Neural Networks for Classification
Jindi Wu, Zeyi Tao, Qun Li
Many recent machine learning tasks resort to quantum computing to improve classification accuracy and training efficiency by taking advantage of quantum mechanics, known as quantum…
MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning
Hua Ma, Qun Li, Yifeng Zheng +5
Federated Learning (FL), a distributed machine learning paradigm, has been adapted to mitigate privacy concerns for customers. Despite their appeal, there are various inference att…
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks
Zeyi Tao, Jindi Wu, Qi Xia +1
Variational quantum algorithms (VQAs) have recently received significant attention from the research community due to their promising performance in Noisy Intermediate-Scale Quantu…
Towards Privacy-Preserving and Verifiable Federated Matrix Factorization
Xicheng Wan, Yifeng Zheng, Qun Li +3
Recent years have witnessed the rapid growth of federated learning (FL), an emerging privacy-aware machine learning paradigm that allows collaborative learning over isolated datase…
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in Machine Learning
Yansong Gao, Qun Li, Yifeng Zheng +3
Training high-performing deep learning models require a rich amount of data which is usually distributed among multiple data sources in practice. Simply centralizing these multi-so…
QuantumFed: A Federated Learning Framework for Collaborative Quantum Training
Qi Xia, Qun Li
With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, dee…