4 citations · 4 across the 2 of their papers we have counts for
6 papers
SurfaceNet: Fault-Tolerant Quantum Networks with Surface Codes
Tianjie Hu, Jindi Wu, Qun Li
Quantum networks serve as the means to transmit information, encoded in quantum bits or qubits, between quantum processors that are physically separated. Given the instability of q…
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…
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…
Two algorithms for compressed sensing of sparse tensors
Shmuel Friedland, Qun Li, Dan Schonfeld +1
Compressed sensing (CS) exploits the sparsity of a signal in order to integrate acquisition and compression. CS theory enables exact reconstruction of a sparse signal from relative…