Search Results for author: Ming Ding

Found 86 papers, 34 papers with code

Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy

no code implementations16 Feb 2024 Songjie Xie, Youlong Wu, Jiaxuan Li, Ming Ding, Khaled B. Letaief

Based on the proposed method, we further develop a variational representation encoding approach that simultaneously achieves fairness and LDP.

Fairness Representation Learning

Private Knowledge Sharing in Distributed Learning: A Survey

no code implementations8 Feb 2024 Yasas Supeksala, Dinh C. Nguyen, Ming Ding, Thilina Ranbaduge, Calson Chua, Jun Zhang, Jun Li, H. Vincent Poor

In this light, it is crucial to utilize information in learning processes that are either distributed or owned by different entities.

CogCoM: Train Large Vision-Language Models Diving into Details through Chain of Manipulations

1 code implementation6 Feb 2024 Ji Qi, Ming Ding, Weihan Wang, Yushi Bai, Qingsong Lv, Wenyi Hong, Bin Xu, Lei Hou, Juanzi Li, Yuxiao Dong, Jie Tang

Vision-Language Models (VLMs) have demonstrated their widespread viability thanks to extensive training in aligning visual instructions to answers.

Visual Reasoning

CogAgent: A Visual Language Model for GUI Agents

1 code implementation14 Dec 2023 Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu, Wenmeng Yu, Junhui Ji, Yan Wang, Zihan Wang, Yuxuan Zhang, Juanzi Li, Bin Xu, Yuxiao Dong, Ming Ding, Jie Tang

People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e. g., computer or smartphone screens.

Language Modelling Visual Question Answering

PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusion

1 code implementation13 Dec 2023 Xin You, Ming Ding, Minghui Zhang, Hanxiao Zhang, Yi Yu, Jie Yang, Yun Gu

Precise boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention, especially for boundary confusion in clinical practice.

Refine, Discriminate and Align: Stealing Encoders via Sample-Wise Prototypes and Multi-Relational Extraction

no code implementations1 Dec 2023 Shuchi Wu, Chuan Ma, Kang Wei, Xiaogang Xu, Ming Ding, Yuwen Qian, Tao Xiang

This paper introduces RDA, a pioneering approach designed to address two primary deficiencies prevalent in previous endeavors aiming at stealing pre-trained encoders: (1) suboptimal performances attributed to biased optimization objectives, and (2) elevated query costs stemming from the end-to-end paradigm that necessitates querying the target encoder every epoch.

DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding

no code implementations11 Nov 2023 Yingjie Niu, Ming Ding, Keisuke Fujii, Kento Ohtani, Alexander Carballo, Kazuya Takeda

The DRUformer is a transformer-based multi-modal important object detection model that takes into account the relationships between all the participants in the driving scenario.

object-detection Object Detection

When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers through Membership Inference Attacks

no code implementations7 Nov 2023 Huan Tian, Guangsheng Zhang, Bo Liu, Tianqing Zhu, Ming Ding, Wanlei Zhou

It leverages the difference in the predictions from both the original and fairness-enhanced models and exploits the observed prediction gaps as attack clues.

Fairness

Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding

no code implementations13 Oct 2023 Jixuan Cui, Jun Li, Zhen Mei, Kang Wei, Sha Wei, Ming Ding, Wen Chen, Song Guo

However, the domain discrepancy and data scarcity problems among clients deteriorate the performance of the global FL model.

Federated Learning Meta-Learning +1

Semantic Difference Guidance for the Uncertain Boundary Segmentation of CT Left Atrial Appendage

1 code implementation MICCAI 2023 Xin You, Ming Ding, Minghui Zhang, Yangqian Wu, Yi Yu, Yun Gu, Jie Yang

In this paper, we have modeled relative relations between the LA and LAA via deep segmentation networks for the first time, and introduce a new LA & LAA CT dataset.

Segmentation

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

no code implementations20 Sep 2023 Shiying Zhang, Jun Li, Long Shi, Ming Ding, Dinh C. Nguyen, Wuzheng Tan, Jian Weng, Zhu Han

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT).

Federated Learning Object Recognition

Sparse Federated Training of Object Detection in the Internet of Vehicles

no code implementations7 Sep 2023 Luping Rao, Chuan Ma, Ming Ding, Yuwen Qian, Lu Zhou, Zhe Liu

However, the current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server, which raises privacy concerns.

Federated Learning Object +2

GPT Can Solve Mathematical Problems Without a Calculator

1 code implementation6 Sep 2023 Zhen Yang, Ming Ding, Qingsong Lv, Zhihuan Jiang, Zehai He, Yuyi Guo, Jinfeng Bai, Jie Tang

Previous studies have typically assumed that large language models are unable to accurately perform arithmetic operations, particularly multiplication of >8 digits, and operations involving decimals and fractions, without the use of calculator tools.

Language Modelling Math

Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing

no code implementations5 Sep 2023 Zhengrong Song, Chuan Ma, Ming Ding, Howard H. Yang, Yuwen Qian, Xiangwei Zhou

This work proposes a novel solution to address these challenges, namely personalized federated deep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization.

Edge-computing Federated Learning +1

Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity

no code implementations4 Aug 2023 Xuefeng Han, Jun Li, Wen Chen, Zhen Mei, Kang Wei, Ming Ding, H. Vincent Poor

With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training.

Federated Learning Scheduling

Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning

no code implementations18 Jul 2023 Kecheng Fan, Wen Chen, Jun Li, Xiumei Deng, Xuefeng Han, Ming Ding

As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users.

Federated Learning Scheduling

R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut

no code implementations18 Jul 2023 Yingjie Niu, Ming Ding, Maoning Ge, Robin Karlsson, Yuxiao Zhang, Kazuya Takeda

Our method aims to improve trust in classification results and empower users to gain a deeper understanding of the model for downstream tasks by providing visualizations of class-specific maps.

Image Classification

G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering

no code implementations8 Jun 2023 Hao Yu, Chuan Ma, Meng Liu, Tianyu Du, Ming Ding, Tao Xiang, Shouling Ji, Xinwang Liu

Through empirical evaluation, comparing G$^2$uardFL with cutting-edge defenses, such as FLAME (USENIX Security 2022) [28] and DeepSight (NDSS 2022) [36], against various backdoor attacks including 3DFed (SP 2023) [20], our results demonstrate its significant effectiveness in mitigating backdoor attacks while having a negligible impact on the aggregated model's performance on benign samples (i. e., the primary task performance).

Anomaly Detection Clustering +2

Learn to Unlearn: A Survey on Machine Unlearning

no code implementations12 May 2023 Youyang Qu, Xin Yuan, Ming Ding, Wei Ni, Thierry Rakotoarivelo, David Smith

This inspired recent research on removing the influence of specific data samples from a trained ML model.

Fairness Machine Unlearning

Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy

no code implementations9 Apr 2023 Kang Wei, Jun Li, Chuan Ma, Ming Ding, Feng Shu, Haitao Zhao, Wen Chen, Hongbo Zhu

Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local training, thereby mitigating the performance degradation induced by DP and and reducing the number of transmission parameters over wireless channels.

Federated Learning Scheduling +1

Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning

no code implementations7 Mar 2023 Xin Yuan, Wei Ni, Ming Ding, Kang Wei, Jun Li, H. Vincent Poor

The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.

Federated Learning Privacy Preserving

Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic

no code implementations27 Feb 2023 Guodong Huang, Chuan Ma, Ming Ding, Yuwen Qian, Chunpeng Ge, Liming Fang, Zhe Liu

To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead.

Website Fingerprinting Attacks

Differentially Private Vertical Federated Learning

no code implementations13 Nov 2022 Thilina Ranbaduge, Ming Ding

Thus, in this paper, we aim to explore how to protect the privacy of individual organisation data in a differential privacy (DP) setting.

Federated Learning

Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control

no code implementations4 Nov 2022 Ziyan Yin, Zhe Wang, Jun Li, Ming Ding, Wen Chen, Shi Jin

The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks.

Federated Learning reinforcement-learning +1

Privacy-preserving Deep Learning based Record Linkage

no code implementations3 Nov 2022 Thilina Ranbaduge, Dinusha Vatsalan, Ming Ding

The global model is then used by a linkage unit to distinguish unlabelled record pairs as matches and non-matches.

Data Integration Privacy Preserving +1

Parameter-Efficient Tuning Makes a Good Classification Head

1 code implementation30 Oct 2022 Zhuoyi Yang, Ming Ding, Yanhui Guo, Qingsong Lv, Jie Tang

In this paper, we find that parameter-efficient tuning makes a good classification head, with which we can simply replace the randomly initialized heads for a stable performance gain.

Classification Natural Language Understanding

How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers

no code implementations20 Oct 2022 Guangsheng Zhang, Bo Liu, Huan Tian, Tianqing Zhu, Ming Ding, Wanlei Zhou

As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale.

Attribute

Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation

1 code implementation21 Sep 2022 Songjie Xie, Shuai Ma, Ming Ding, Yuanming Shi, Mingjian Tang, Youlong Wu

Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver.

Informativeness

CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

1 code implementation29 May 2022 Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, Jie Tang

Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation.

Text-to-Video Generation Video Generation

CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

1 code implementation28 Apr 2022 Ming Ding, Wendi Zheng, Wenyi Hong, Jie Tang

The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images.

Language Modelling Super-Resolution +1

Vertical Federated Learning: Challenges, Methodologies and Experiments

no code implementations9 Feb 2022 Kang Wei, Jun Li, Chuan Ma, Ming Ding, Sha Wei, Fan Wu, Guihai Chen, Thilina Ranbaduge

As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different clients.

Federated Learning

Negative-ResNet: Noisy Ambulatory Electrocardiogram Signal Classification Scheme

no code implementations25 Jan 2022 Zijiao Chen, Zihuai Lin, Peng Wang, Ming Ding

With recently successful applications of deep learning in computer vision and general signal processing, deep learning has shown many unique advantages in medical signal processing.

Classification

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks

1 code implementation30 Dec 2021 Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang

Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements.

Benchmarking

Adaptive Diffusion in Graph Neural Networks

no code implementations NeurIPS 2021 Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang

Notably, message passing based GNNs, e. g., graph convolutional networks, leverage the immediate neighbors of each node during the aggregation process, and recently, graph diffusion convolution (GDC) is proposed to expand the propagation neighborhood by leveraging generalized graph diffusion.

Federated Learning for Smart Healthcare: A Survey

no code implementations16 Nov 2021 Dinh C. Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia A. Dobre, Won-Joo Hwang

Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI).

Federated Learning Management

Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning

no code implementations29 Sep 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining.

Edge-computing

BEdgeHealth: A Decentralized Architecture for Edge-based IoMT Networks Using Blockchain

no code implementations29 Sep 2021 Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne

The healthcare industry has witnessed significant transformations in e-health services by using mobile edge computing (MEC) and blockchain to facilitate healthcare operations.

Edge-computing

6G Internet of Things: A Comprehensive Survey

no code implementations11 Aug 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, Octavia Dobre, H. Vincent Poor

The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.

Autonomous Driving

Low-Latency Federated Learning over Wireless Channels with Differential Privacy

no code implementations20 Jun 2021 Kang Wei, Jun Li, Chuan Ma, Ming Ding, Cailian Chen, Shi Jin, Zhu Han, H. Vincent Poor

Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach.

Federated Learning

Federated Learning for Industrial Internet of Things in Future Industries

no code implementations31 May 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, H. Vincent Poor

The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.

Federated Learning

M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers

no code implementations NeurIPS 2021 Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang

Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.

Image Generation

CogView: Mastering Text-to-Image Generation via Transformers

4 code implementations NeurIPS 2021 Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang

Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding.

Ranked #56 on Text-to-Image Generation on MS COCO (using extra training data)

Super-Resolution Zero-Shot Text-to-Image Generation

UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis

no code implementations NeurIPS 2021 Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang

Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.

Image Generation

Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design

1 code implementation10 May 2021 Chuan Ma, Jun Li, Ming Ding, Kang Wei, Wen Chen, H. Vincent Poor

Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients.

Federated Learning

Federated Learning for Internet of Things: A Comprehensive Survey

no code implementations16 Apr 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).

Federated Learning

Realistic Differentially-Private Transmission Power Flow Data Release

1 code implementation25 Mar 2021 David Smith, Frederik Geth, Elliott Vercoe, Andrew Feutrill, Ming Ding, Jonathan Chan, James Foster, Thierry Rakotoarivelo

For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers.

GLM: General Language Model Pretraining with Autoregressive Blank Infilling

9 code implementations ACL 2022 Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang

On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1. 25x parameters of BERT Large , demonstrating its generalizability to different downstream tasks.

Ranked #4 on Language Modelling on WikiText-103 (using extra training data)

Abstractive Text Summarization Classification +4

GPT Understands, Too

6 code implementations18 Mar 2021 Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, Jie Tang

Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU).

Knowledge Probing Language Modelling +2

DP-Image: Differential Privacy for Image Data in Feature Space

no code implementations12 Mar 2021 Hanyu Xue, Bo Liu, Ming Ding, Tianqing Zhu, Dayong Ye, Li Song, Wanlei Zhou

The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public.

IdentityDP: Differential Private Identification Protection for Face Images

no code implementations2 Mar 2021 Yunqian Wen, Li Song, Bo Liu, Ming Ding, Rong Xie

We propose IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy (DP) mechanism.

De-identification Disentanglement +2

M6: A Chinese Multimodal Pretrainer

no code implementations1 Mar 2021 Junyang Lin, Rui Men, An Yang, Chang Zhou, Ming Ding, Yichang Zhang, Peng Wang, Ang Wang, Le Jiang, Xianyan Jia, Jie Zhang, Jianwei Zhang, Xu Zou, Zhikang Li, Xiaodong Deng, Jie Liu, Jinbao Xue, Huiling Zhou, Jianxin Ma, Jin Yu, Yong Li, Wei Lin, Jingren Zhou, Jie Tang, Hongxia Yang

In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.

Image Generation

Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization

no code implementations28 Jan 2021 Kang Wei, Jun Li, Ming Ding, Chuan Ma, Yo-Seb Jeon, H. Vincent Poor

An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP).

Federated Learning Model Poisoning

Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

no code implementations18 Jan 2021 Jun Li, Yumeng Shao, Kang Wei, Ming Ding, Chuan Ma, Long Shi, Zhu Han, H. Vincent Poor

Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.

Federated Learning

Generalizing Graph Convolutional Networks

1 code implementation1 Jan 2021 Jialin Zhao, Yuxiao Dong, Jie Tang, Ming Ding, Kuansan Wang

Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs.

F-manifold color algebras

no code implementations22 Dec 2020 Ming Ding, Zhiqi Chen, Jifu Li

In this paper, we introduce the notion of F-manifold color algebras and study their properties which extend some results for F-manifold algebras.

Rings and Algebras

Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients

no code implementations2 Dec 2020 Jun Li, Yumeng Shao, Ming Ding, Chuan Ma, Kang Wei, Zhu Han, H. Vincent Poor

The proposed BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning.

Federated Learning

CogLTX: Applying BERT to Long Texts

1 code implementation NeurIPS 2020 Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang

BERTs are incapable of processing long texts due to its quadratically increasing memory and time consumption.

text-classification Text Classification

When Machine Learning Meets Privacy: A Survey and Outlook

no code implementations24 Nov 2020 Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, Zihuai Lin

The newly emerged machine learning (e. g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems.

BIG-bench Machine Learning

When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm

no code implementations20 Sep 2020 Chuan Ma, Jun Li, Ming Ding, Long Shi, Taotao Wang, Zhu Han, H. Vincent Poor

Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged.

Networking and Internet Architecture

RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network

1 code implementation4 Jul 2020 Chuan Ma, Jun Li, Ming Ding, Bo Liu, Kang Wei, Jian Weng, H. Vincent Poor

Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection.

Generative Adversarial Network

Understanding Negative Sampling in Graph Representation Learning

4 code implementations20 May 2020 Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, Jie Tang

To the best of our knowledge, we are the first to derive the theory and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution.

Graph Learning Graph Representation Learning +2

User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization

no code implementations29 Feb 2020 Kang Wei, Jun Li, Ming Ding, Chuan Ma, Hang Su, Bo Zhang, H. Vincent Poor

According to our analysis, the UDP framework can realize $(\epsilon_{i}, \delta_{i})$-LDP for the $i$-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes.

Federated Learning Privacy Preserving

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

no code implementations1 Nov 2019 Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farokhi Farhad, Shi Jin, Tony Q. S. Quek, H. Vincent Poor

Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.

Federated Learning Privacy Preserving +1

On Safeguarding Privacy and Security in the Framework of Federated Learning

no code implementations14 Sep 2019 Chuan Ma, Jun Li, Ming Ding, Howard Hao Yang, Feng Shu, Tony Q. S. Quek, H. Vincent Poor

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL).

Networking and Internet Architecture

Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning

no code implementations15 Aug 2019 Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne

How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem.

Management reinforcement-learning +1

Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning

no code implementations15 Aug 2019 Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne

Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications.

Edge-computing reinforcement-learning +1

Towards Knowledge-Based Recommender Dialog System

1 code implementation IJCNLP 2019 Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang

In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System.

Recommendation Systems Text Generation

Cognitive Knowledge Graph Reasoning for One-shot Relational Learning

1 code implementation13 Jun 2019 Zhengxiao Du, Chang Zhou, Ming Ding, Hongxia Yang, Jie Tang

Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently.

Knowledge Graphs Relational Reasoning +1

Privacy Preserving Location Data Publishing: A Machine Learning Approach

no code implementations24 Feb 2019 Sina Shaham, Ming Ding, Bo Liu, Shuping Dang, Zihuai Lin, Jun Li

By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use $k$-means algorithm for this purpose.

BIG-bench Machine Learning Clustering +2

Semi-supervised Learning on Graphs with Generative Adversarial Nets

2 code implementations1 Sep 2018 Ming Ding, Jie Tang, Jie Zhang

We first provide insights on working principles of adversarial learning over graphs and then present GraphSGAN, a novel approach to semi-supervised learning on graphs.

Spectral Network Embedding: A Fast and Scalable Method via Sparsity

1 code implementation7 Jun 2018 Jie Zhang, Yan Wang, Jie Tang, Ming Ding

In this paper, we propose a $10\times \sim 100\times$ faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis.

Link Prediction Network Embedding +1

Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model

no code implementations16 May 2018 Sina Shaham, Ming Ding, Bo Liu, Zihuai Lin, Jun Li

In this paper, we incorporate a new type of side information based on consecutive location changes of users and propose a new metric called transition-entropy to investigate the location privacy preservation, followed by two algorithms to improve the transition-entropy for a given dummy generation algorithm.

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