Search Results for author: Xiaohu Tang

Found 7 papers, 1 papers with code

Object Segmentation-Assisted Inter Prediction for Versatile Video Coding

no code implementations18 Mar 2024 Zhuoyuan Li, Zikun Yuan, Li Li, Dong Liu, Xiaohu Tang, Feng Wu

Moreover, segmentation mask is considered in the joint rate-distortion optimization for motion estimation and partition estimation to derive the motion vector of different regions and partition more accurately.

Motion Compensation Motion Estimation +3

Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

no code implementations23 Aug 2023 Ranggi Hwang, Jianyu Wei, Shijie Cao, Changho Hwang, Xiaohu Tang, Ting Cao, Mao Yang

To tackle the high compute requirements of LLMs, the Mixture-of-Experts (MoE) architecture was introduced which is able to scale its model size without proportionally scaling up its computational requirements.

An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning

no code implementations21 Jun 2023 Cheng Yang, Xue Yang, Dongxian Wu, Xiaohu Tang

Then the server aggregates all the proxy datasets to form a central dummy dataset, which is used to finetune aggregated global model.

Federated Learning

An Efficient and Multi-private Key Secure Aggregation for Federated Learning

no code implementations15 Jun 2023 Xue Yang, Zifeng Liu, Xiaohu Tang, Rongxing Lu, Bo Liu

With the emergence of privacy leaks in federated learning, secure aggregation protocols that mainly adopt either homomorphic encryption or threshold secret sharing have been widely developed for federated learning to protect the privacy of the local training data of each client.

Federated Learning

Low-Complexity Signal Detection for the Splitting Receiver Scheme

no code implementations30 May 2023 Yanyan Wang, Qidi Li, Xiaohu Tang

Based on the three-dimensional (3D) received signal of the splitting receiver, we derive an equivalent two-dimensional (2D) signal model and develop a low-complexity signal detection method for the practical modulation scheme.

An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning

1 code implementation23 Feb 2020 Xue Yang, Yan Feng, Weijun Fang, Jun Shao, Xiaohu Tang, Shu-Tao Xia, Rongxing Lu

However, the strong defence ability and high learning accuracy of these schemes cannot be ensured at the same time, which will impede the wide application of FL in practice (especially for medical or financial institutions that require both high accuracy and strong privacy guarantee).

Federated Learning

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