72 citations · 222 across the 12 of their papers we have counts for
5 papers · 1 filter
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
Jingwei Sun, Ang Li, Binghui Wang +3
Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. However, recent works demonstrated that sh…
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
Binghui Wang, Ang Li, Hai Li +1
Graph-based semi-supervised node classification (GraphSSC) has wide applications, ranging from networking and security to data mining and machine learning, etc. However, existing c…
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets
Ang Li, Jingwei Sun, Binghui Wang +4
Federated learning is a popular distributed machine learning paradigm with enhanced privacy. Its primary goal is learning a global model that offers good performance for the partic…
TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations
Ang Li, Yixiao Duan, Huanrui Yang +2
The success of deep learning partially benefits from the availability of various large-scale datasets. These datasets are often crowdsourced from individual users and contain priva…
Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification
Huanrui Yang, Minxue Tang, Wei Wen +5
Modern deep neural networks (DNNs) often require high memory consumption and large computational loads. In order to deploy DNN algorithms efficiently on edge or mobile devices, a s…