b'Qiang Yang'

research

∙ 04/21/2022

Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks

Industrial control systems (ICSs) are facing increasing cyber-physical a...

Tao Yang, et al. ∙

research

∙ 03/11/2022

No free lunch theorem for security and utility in federated learning

In a federated learning scenario where multiple parties jointly learn a ...

Xiaojin Zhang, et al. ∙

research

∙ 03/10/2022

A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges

Dubbed "the successor to the mobile Internet", the concept of the Metave...

Minrui Xu, et al. ∙

research

∙ 01/05/2022

Realizing the Metaverse with Edge Intelligence: A Match Made in Heaven

Dubbed "the successor to the mobile Internet", the concept of the Metave...

Wei Yang Bryan Lim, et al. ∙

research

∙ 12/15/2021

LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization

Federated optimization (FedOpt), which targets at collaboratively traini...

Huiming Chen, et al. ∙

research

∙ 10/12/2021

SportsSum2.0: Generating High-Quality Sports News from Live Text Commentary

Sports game summarization aims to generate news articles from live text ...

Jiaan Wang, et al. ∙

research

∙ 09/27/2021

FedIPR: Ownership Verification for Federated Deep Neural Network Models

Federated learning models must be protected against plagiarism since the...

Lixin Fan, et al. ∙

research

∙ 09/19/2021

Multi-Task Learning in Natural Language Processing: An Overview

Deep learning approaches have achieved great success in the field of Nat...

Shijie Chen, et al. ∙

research

∙ 08/19/2021

QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction

We study the problem of query attribute value extraction, which aims to ...

Danqing Zhang, et al. ∙

research

∙ 08/18/2021

Practical and Secure Federated Recommendation with Personalized Masks

Federated recommendation is a new notion of private distributed recommen...

Liu Yang, et al. ∙

research

∙ 08/06/2021

Transferring Knowledge Distillation for Multilingual Social Event Detection

Recently published graph neural networks (GNNs) show promising performan...

Jiaqian Ren, et al. ∙

research

∙ 06/30/2021

CityNet: A Multi-city Multi-modal Dataset for Smart City Applications

Data-driven approaches have been applied to many problems in urban compu...

Xu Geng, et al. ∙

research

∙ 06/17/2021

Frustratingly Easy Transferability Estimation

Transferability estimation has been an essential tool in selecting a pre...

Long-Kai Huang, et al. ∙

research

∙ 05/19/2021

Federated Singular Vector Decomposition

With the promulgation of data protection laws (e.g., GDPR in 2018), priv...

Di Chai, et al. ∙

research

∙ 03/16/2021

Ternary Hashing

This paper proposes a novel ternary hash encoding for learning to hash m...

Chang Liu, et al. ∙

research

∙ 03/01/2021

Towards Personalized Federated Learning

As artificial intelligence (AI)-empowered applications become widespread...

Alysa Ziying Tan, et al. ∙

research

∙ 02/17/2021

A Game-theoretic Approach Towards Collaborative Coded Computation Offloading

Coded distributed computing (CDC) has emerged as a promising approach be...

Jer Shyuan Ng, et al. ∙

research

∙ 02/08/2021

Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack

Ever since Machine Learning as a Service (MLaaS) emerges as a viable bus...

Ding Sheng Ong, et al. ∙

research

∙ 01/12/2021

TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation

We investigate how to solve the cross-corpus news recommendation for uns...

Guangneng Hu, et al. ∙

research

∙ 12/07/2020

Privacy and Robustness in Federated Learning: Attacks and Defenses

As data are increasingly being stored in different silos and societies b...

Lingjuan Lyu, et al. ∙

research

∙ 11/27/2020

Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness

Deep neural networks (DNNs) are known to be prone to adversarial attacks...

Yilun Jin, et al. ∙

research

∙ 11/19/2020

FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning

As an innovative solution for privacy-preserving machine learning (ML), ...

Di Chai, et al. ∙

research

∙ 10/16/2020

PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation

Transfer learning is an effective technique to improve a target recommen...

Guangneng Hu, et al. ∙

research

∙ 08/25/2020

Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning

Transfer learning has become a common practice for training deep learnin...

Yinghua Zhang, et al. ∙

research

∙ 08/25/2020

Protect, Show, Attend and Tell: Image Captioning Model with Ownership Protection

By and large, existing Intellectual Property Right (IPR) protection on d...

Jian Han Lim, et al. ∙

research

∙ 07/03/2020

Privacy Threats Against Federated Matrix Factorization

Matrix Factorization has been very successful in practical recommendatio...

Dashan Gao, et al. ∙

research

∙ 06/20/2020

Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks

This paper investigates capabilities of Privacy-Preserving Deep Learning...

Lixin Fan, et al. ∙

research

∙ 06/18/2020

SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic

Since the first alert launched by the World Health Organization (5 Janua...

Qiang Yang, et al. ∙

research

∙ 05/31/2020

Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning

In 5G and Beyond networks, Artificial Intelligence applications are expe...

Wei Yang Bryan Lim, et al. ∙

research

∙ 05/22/2020

Graph Random Neural Network

Graph neural networks (GNNs) have generalized deep learning methods into...

Wenzheng Feng, et al. ∙

research

∙ 05/20/2020

Network On Network for Tabular Data Classification in Real-world Applications

Tabular data is the most common data format adopted by our customers ran...

Yuanfei Luo, et al. ∙

research

∙ 03/12/2020

Fisher Deep Domain Adaptation

Deep domain adaptation models learn a neural network in an unlabeled tar...

Yinghua Zhang, et al. ∙

research

∙ 03/04/2020

Threats to Federated Learning: A Survey

With the emergence of data silos and popular privacy awareness, the trad...

Lingjuan Lyu, et al. ∙

research

∙ 02/26/2020

A Survey towards Federated Semi-supervised Learning

The success of Artificial Intelligence (AI) should be largely attributed...

Yilun Jin, et al. ∙

research

∙ 01/24/2020

Mechanism Design for Multi-Party Machine Learning

In a multi-party machine learning system, different parties cooperate on...

Mengjing Chen, et al. ∙

research

∙ 01/23/2020

RPN: A Residual Pooling Network for Efficient Federated Learning

Federated learning is a new machine learning framework which enables dif...

Anbu Huang, et al. ∙

research

∙ 01/17/2020

FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

Visual object detection is a computer vision-based artificial intelligen...

Yang Liu, et al. ∙

research

∙ 12/24/2019

A Communication Efficient Vertical Federated Learning Framework

One critical challenge for applying today's Artificial Intelligence (AI)...

Yang Liu, et al. ∙

research

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

Peter Kairouz, et al. ∙

research

∙ 12/01/2019

A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression

Data privacy and security becomes a major concern in building machine le...

Kai Yang, et al. ∙

research

∙ 10/31/2019

Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning

Joint extraction of aspects and sentiments can be effectively formulated...

Zheng Li, et al. ∙

research

∙ 10/25/2019

L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition

Modern Automatic Speech Recognition (ASR) systems primarily rely on scor...

Yuanfeng Song, et al. ∙

research

∙ 10/14/2019

Federated Transfer Reinforcement Learning for Autonomous Driving

Reinforcement learning (RL) is widely used in autonomous driving tasks a...

Xinle Liang, et al. ∙

research

∙ 09/26/2019

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

In recent years, mobile devices are equipped with increasingly advanced ...

Wei Yang Bryan Lim, et al. ∙

research

∙ 09/17/2019

Transfer Learning with Dynamic Distribution Adaptation

Transfer learning aims to learn robust classifiers for the target domain...

Jindong Wang, et al. ∙

research

∙ 09/11/2019

HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography

Electroencephalography (EEG) classification techniques have been widely ...

Dashan Gao, et al. ∙

research

∙ 06/12/2019

Secure Federated Matrix Factorization

To protect user privacy and meet law regulations, federated (machine) le...

Di Chai, et al. ∙

research

∙ 05/28/2019

Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction

An effective content recommendation in modern social media platforms sho...

Wenyi Xiao, et al. ∙

research

∙ 05/27/2019

Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting

Graph convolution network based approaches have been recently used to mo...

Xu Geng, et al. ∙

research

∙ 04/02/2019

Easy Transfer Learning By Exploiting Intra-domain Structures

Transfer learning aims at transferring knowledge from a well-labeled dom...

Jindong Wang, et al. ∙

Qiang Yang

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