11 citations · 34 across the 10 of their papers we have counts for
10 papers
PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks
Shiwei Ding, Lan Zhang, Miao Pan +1
Collaborative inference has been a promising solution to enable resource-constrained edge devices to perform inference using state-of-the-art deep neural networks (DNNs). In collab…
Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning
Gaurav Bagwe, Xiaoyong Yuan, Miao Pan +1
Federated continual learning (FCL) learns incremental tasks over time from confidential datasets distributed across clients. This paper focuses on rehearsal-free FCL, which has sev…
Distributed Pruning Towards Tiny Neural Networks in Federated Learning
Hong Huang, Lan Zhang, Chaoyue Sun +3
Neural network pruning is an essential technique for reducing the size and complexity of deep neural networks, enabling large-scale models on devices with limited resources. Howeve…
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer
Madhureeta Das, Xianhao Chen, Xiaoyong Yuan +1
Given the rapidly changing machine learning environments and expensive data labeling, semi-supervised domain adaptation (SSDA) is imperative when the labeled data from the source d…
Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning
Gaurav Bagwe, Jian Li, Xiaoyong Yuan +1
Despite the success of AI-enabled onboard perception, on-ramp merging has been one of the main challenges for autonomous driving. Due to limited sensing range of onboard sensors, a…
Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection
Saket S. Chaturvedi, Lan Zhang, Xiaoyong Yuan
Despite the recent advances of deep neural networks, object detection for adverse weather remains challenging due to the poor perception of some sensors in adverse weather. Instead…
Membership Inference Attacks and Defenses in Neural Network Pruning
Xiaoyong Yuan, Lan Zhang
Neural network pruning has been an essential technique to reduce the computation and memory requirements for using deep neural networks for resource-constrained devices. Most exist…
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models
Lan Zhang, Dapeng Wu, Xiaoyong Yuan
Federated learning enables multiple distributed devices to collaboratively learn a shared prediction model without centralizing their on-device data. Most of the current algorithms…
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks
Xiaoyong Yuan, Lei Ding, Malek Ben Salem +2
Recently web applications have been widely used in enterprises to assist employees in providing effective and efficient business processes. Forecasting upcoming web events in enter…
ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles
Xiaoyong Yuan, Leah Ding, Lan Zhang +2
Deep neural networks (DNNs) have become the essential components for various commercialized machine learning services, such as Machine Learning as a Service (MLaaS). Recent studies…