294 citations · 573 across the 32 of their papers we have counts for
11 papers · 1 filter
Discovering Multiscale Deep Formulas in Complex Systems via Neural-Guided Lambda Calculus
Hanqiao Yu, Shusen Yang, Xuebin Ren +1
A fundamental problem in science is identifying underlying patterns of complex systems in the form of concise mathematical formulas. Current Artificial Intelligence (AI)-based meth…
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
Zihao Zhou, Shusen Yang, Fangyuan Zhao +1
Graph federated learning enables the collaborative extraction of high-order information from distributed subgraphs while preserving the privacy of raw data. However, graph data oft…
Rethinking Layer-wise Gaussian Noise Injection: Bridging Implicit Objectives and Privacy Budget Allocation
Qifeng Tan, Shusen Yang, Xuebin Ren +1
Layer-wise Gaussian mechanisms (LGM) enhance flexibility in differentially private deep learning by injecting noise into partitioned gradient vectors. However, existing methods oft…
Review of Mathematical Optimization in Federated Learning
Shusen Yang, Fangyuan Zhao, Zihao Zhou +3
Federated Learning (FL) has been becoming a popular interdisciplinary research area in both applied mathematics and information sciences. Mathematically, FL aims to collaboratively…
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning
Jie Shen, Shusen Yang, Cong Zhao +5
Intelligent equipment fault diagnosis based on Federated Transfer Learning (FTL) attracts considerable attention from both academia and industry. It allows real-world industrial ag…
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data
Zihao Zhou, Yanan Li, Xuebin Ren +1
Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considerin…
Latent Dirichlet Allocation Model Training with Differential Privacy
Fangyuan Zhao, Xuebin Ren, Shusen Yang +3
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various app…
CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning
Yuanrui Dong, Peng Zhao, Hanqiao Yu +2
The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, respons…
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
Yanan Li, Shusen Yang, Xuebin Ren +1
Federated learning has been showing as a promising approach in paving the last mile of artificial intelligence, due to its great potential of solving the data isolation problem in…
On Privacy Protection of Latent Dirichlet Allocation Model Training
Fangyuan Zhao, Xuebin Ren, Shusen Yang +1
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine…
Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis
Yanan Li, Xuebin Ren, Shusen Yang +1
It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate…