204 citations · 388 across the 43 of their papers we have counts for
19 papers · 1 filter
FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Management
Kahou Tam, Chunlin Tian, Li Li +2
Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices to collaboratively train a shared model while preserving data privacy. However, one fundame…
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
Chunlin Tian, Zhan Shi, Xinpeng Qin +2
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy. The selection of participating devices in each training round…
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Tianrui Qin, Xitong Gao, Juanjuan Zhao +2
Unlearnable example attacks are data poisoning techniques that can be used to safeguard public data against unauthorized use for training deep learning models. These methods add st…
Toward Efficient Automated Feature Engineering
Kafeng Wang, Pengyang Wang, Chengzhong xu
Automated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applicatio…
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack
Yunrui Yu, Xitong Gao, Cheng-Zhong Xu
Adversarial attacks can deceive neural networks by adding tiny perturbations to their input data. Ensemble defenses, which are trained to minimize attack transferability among sub-…
Deep Active Learning with Noise Stability
Xingjian Li, Pengkun Yang, Yangcheng Gu +4
Uncertainty estimation for unlabeled data is crucial to active learning. With a deep neural network employed as the backbone model, the data selection process is highly challenging…
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction
Liang Gao, Huazhu Fu, Li Li +3
Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated le…
Boosting Active Learning via Improving Test Performance
Tianyang Wang, Xingjian Li, Pengkun Yang +5
Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless,…
SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing
Kafeng Wang, Haoyi Xiong, Jie Zhang +3
The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, includi…
Federated Noisy Client Learning
Kahou Tam, Li Li, Bo Han +2
Federated learning (FL) collaboratively trains a shared global model depending on multiple local clients, while keeping the training data decentralized in order to preserve data pr…
LAFEAT: Piercing Through Adversarial Defenses with Latent Features
Yunrui Yu, Xitong Gao, Cheng-Zhong Xu
Deep convolutional neural networks are susceptible to adversarial attacks. They can be easily deceived to give an incorrect output by adding a tiny perturbation to the input. This…
SMILE: Self-Distilled MIxup for Efficient Transfer LEarning
Xingjian Li, Haoyi Xiong, Chengzhong Xu +1
To improve the performance of deep learning, mixup has been proposed to force the neural networks favoring simple linear behaviors in-between training samples. Performing mixup for…
DEAL: Decremental Energy-Aware Learning in a Federated System
Wenting Zou, Li Li, Zichen Xu +1
Federated learning struggles with their heavy energy footprint on battery-powered devices. The learning process keeps all devices awake while draining expensive battery power to tr…
Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement
Xingjian Li, Di Hu, Xuhong Li +5
Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples. To obtain b…
XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup
Xingjian Li, Haoyi Xiong, Haozhe An +2
Transferring knowledge from large source datasets is an effective way to fine-tune the deep neural networks of the target task with a small sample size. A great number of algorithm…
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr
Xingjian Li, Haoyi Xiong, Haozhe An +2
Fine-tuning the deep convolution neural network(CNN) using a pre-trained model helps transfer knowledge learned from larger datasets to the target task. While the accuracy could be…
COLAM: Co-Learning of Deep Neural Networks and Soft Labels via Alternating Minimization
Xingjian Li, Haoyi Xiong, Haozhe An +2
Softening labels of training datasets with respect to data representations has been frequently used to improve the training of deep neural networks (DNNs). While such a practice ha…
Focused Quantization for Sparse CNNs
Yiren Zhao, Xitong Gao, Daniel Bates +2
Deep convolutional neural networks (CNNs) are powerful tools for a wide range of vision tasks, but the enormous amount of memory and compute resources required by CNNs pose a chall…
Sitatapatra: Blocking the Transfer of Adversarial Samples
Ilia Shumailov, Xitong Gao, Yiren Zhao +3
Convolutional Neural Networks (CNNs) are widely used to solve classification tasks in computer vision. However, they can be tricked into misclassifying specially crafted `adversari…