Search Results for author: Lichao Sun

Found 43 papers, 15 papers with code

Data-Free Adversarial Knowledge Distillation for Graph Neural Networks

no code implementations8 May 2022 Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi, Carl Yang, Lichao Sun

Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications.

Graph Classification Knowledge Distillation +2

Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation

1 code implementation17 Mar 2022 Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Lichao Sun

Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well studied as other domains, such as computer vision and natural language processing.

Entity Embeddings Federated Learning +2

FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization

no code implementations29 Nov 2021 Dezhong Yao, Wanning Pan, Yao Wan, Hai Jin, Lichao Sun

The underlying assumption of recent federated learning (FL) paradigms is that local models usually share the same network architecture as the global model, which becomes impractical for mobile and IoT devices with different setups of hardware and infrastructure.

Distributed Computing Federated Learning +3

Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding

no code implementations Findings (ACL) 2022 Sijia Wang, Mo Yu, Shiyu Chang, Lichao Sun, Lifu Huang

Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols.

Event Extraction Multi-class Classification

DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems

no code implementations7 Oct 2021 Ce Zhou, Qiben Yan, Yan Shi, Lichao Sun

By exploiting the weaknesses of the stereo matching in depth estimation algorithms and the lens flare effect in optical imaging, we propose DoubleStar, a long-range attack that injects fake obstacle depth by projecting pure light from two complementary light sources.

Depth Estimation Stereo Matching

FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning

no code implementations29 Sep 2021 Yutong Dai, Xingjun Ma, Lichao Sun

Federated learning (FL) is a privacy-aware collaborative learning paradigm that allows multiple parties to jointly train a machine learning model without sharing their private data.

Federated Learning

Source Inference Attacks in Federated Learning

1 code implementation13 Sep 2021 Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Xuyun Zhang

However, existing MIAs ignore the source of a training member, i. e., the information of which client owns the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients.

Federated Learning Inference Attack

How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data

no code implementations ICLR 2022 Zhiyuan Zhang, Lingjuan Lyu, Weiqiang Wang, Lichao Sun, Xu sun

In this work, we observe an interesting phenomenon that the variations of parameters are always AWPs when tuning the trained clean model to inject backdoors.

DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

1 code implementation26 Aug 2021 Yu Wang, Zhiwei Liu, Ziwei Fan, Lichao Sun, Philip S. Yu

In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information.

Knowledge Graphs Recommendation Systems

Multiplex Graph Networks for Multimodal Brain Network Analysis

1 code implementation31 Jul 2021 Zhaoming Kong, Lichao Sun, Hao Peng, Liang Zhan, Yong Chen, Lifang He

In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis.

Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis

no code implementations7 Jul 2021 Yanqiao Zhu, Hejie Cui, Lifang He, Lichao Sun, Carl Yang

Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis.

Contrastive Learning

Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data

no code implementations30 Jun 2021 Dezhong Yao, Wanning Pan, Yutong Dai, Yao Wan, Xiaofeng Ding, Hai Jin, Zheng Xu, Lichao Sun

Federated learning enables multiple clients to collaboratively learn a global model by periodically aggregating the clients' models without transferring the local data.

Federated Learning Knowledge Distillation

Subgraph Federated Learning with Missing Neighbor Generation

no code implementations NeurIPS 2021 Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu Ming Yiu

Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions.

Federated Learning Graph Mining

Federated Multi-View Learning for Private Medical Data Integration and Analysis

no code implementations4 May 2021 Sicong Che, Hao Peng, Lichao Sun, Yong Chen, Lifang He

This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV).

Federated Learning MULTI-VIEW LEARNING

User Preference-aware Fake News Detection

1 code implementation25 Apr 2021 Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, Lichao Sun

The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.

Fact Checking Fake News Detection +2

Membership Inference Attacks on Knowledge Graphs

no code implementations16 Apr 2021 Yu Wang, Lifu Huang, Philip S. Yu, Lichao Sun

Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.

Inference Attack Knowledge Graph Completion +4

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

1 code implementation14 Apr 2021 Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr

FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.

Federated Learning Molecular Property Prediction

Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!

1 code implementation NAACL 2021 Xuanli He, Lingjuan Lyu, Qiongkai Xu, Lichao Sun

Finally, we investigate two defence strategies to protect the victim model and find that unless the performance of the victim model is sacrificed, both model ex-traction and adversarial transferability can effectively compromise the target models

Model extraction Text Classification +1

Membership Inference Attacks on Machine Learning: A Survey

1 code implementation14 Mar 2021 Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Philip S. Yu, Xuyun Zhang

In recent years, MIAs have been shown to be effective on various ML models, e. g., classification models and generative models.

Fairness Image Classification +3

FedMood: Federated Learning on Mobile Health Data for Mood Detection

1 code implementation6 Feb 2021 Xiaohang Xu, Hao Peng, Lichao Sun, Md Zakirul Alam Bhuiyan, Lianzhong Liu, Lifang He

Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research.

Depression Detection Federated Learning +1

EXPLORING VULNERABILITIES OF BERT-BASED APIS

no code implementations1 Jan 2021 Xuanli He, Lingjuan Lyu, Lichao Sun, Xiaojun Chang, Jun Zhao

We then demonstrate how the extracted model can be exploited to develop effective attribute inference attack to expose sensitive information of the training data.

Inference Attack Model extraction +2

Privacy and Robustness in Federated Learning: Attacks and Defenses

no code implementations7 Dec 2020 Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu

Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries.

Federated Learning

Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks

no code implementations COLING 2020 Lichao Sun, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu, Lifang He

Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.

Data Augmentation Image Classification

Secure Network Release with Link Privacy

no code implementations28 Sep 2020 Carl Yang, Haonan Wang, Ke Zhang, Lichao Sun

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).

Graph Generation

Federated Model Distillation with Noise-Free Differential Privacy

no code implementations11 Sep 2020 Lichao Sun, Lingjuan Lyu

Conventional federated learning directly averages model weights, which is only possible for collaboration between models with homogeneous architectures.

Federated Learning

LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy

no code implementations31 Jul 2020 Lichao Sun, Jianwei Qian, Xun Chen

In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues.

Federated Learning

Natural Backdoor Attack on Text Data

no code implementations29 Jun 2020 Lichao Sun

Recently, advanced NLP models have seen a surge in the usage of various applications.

Backdoor Attack Text Classification

Secure Deep Graph Generation with Link Differential Privacy

1 code implementation1 May 2020 Carl Yang, Haonan Wang, Ke Zhang, Liang Chen, Lichao Sun

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).

Graph Generation Link Prediction

SplitFed: When Federated Learning Meets Split Learning

1 code implementation25 Apr 2020 Chandra Thapa, M. A. P. Chamikara, Seyit Camtepe, Lichao Sun

SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server.

Federated Learning

Differentially Private Deep Learning with Smooth Sensitivity

no code implementations1 Mar 2020 Lichao Sun, Yingbo Zhou, Philip S. Yu, Caiming Xiong

Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice.

Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT

no code implementations27 Feb 2020 Lichao Sun, Kazuma Hashimoto, Wenpeng Yin, Akari Asai, Jia Li, Philip Yu, Caiming Xiong

There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously.

Question Answering Sentiment Analysis

Near-Zero-Cost Differentially Private Deep Learning with Teacher Ensembles

no code implementations25 Sep 2019 Lichao Sun, Yingbo Zhou, Jia Li, Richard Socher, Philip S. Yu, Caiming Xiong

Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice.

Private Deep Learning with Teacher Ensembles

no code implementations5 Jun 2019 Lichao Sun, Yingbo Zhou, Ji Wang, Jia Li, Richard Sochar, Philip S. Yu, Caiming Xiong

Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information.

Ensemble Learning Knowledge Distillation +1

Self-Activation Influence Maximization

no code implementations5 Jun 2019 Lichao Sun, Albert Chen, Philip S. Yu, Wei Chen

We incorporate self activation into influence propagation and propose the self-activation independent cascade (SAIC) model: nodes may be self activated besides being selected as seeds, and influence propagates from both selected seeds and self activated nodes.

Social and Information Networks

Adversarial Attack and Defense on Graph Data: A Survey

1 code implementation26 Dec 2018 Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Lifang He, Bo Li

Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph.

Adversarial Attack Image Classification +1

Private Model Compression via Knowledge Distillation

no code implementations13 Nov 2018 Ji Wang, Weidong Bao, Lichao Sun, Xiaomin Zhu, Bokai Cao, Philip S. Yu

To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA.

Knowledge Distillation Model Compression +1

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

no code implementations11 Sep 2018 Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, Philip S. Yu

Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.

Network Embedding Tensor Decomposition

Deep Learning Towards Mobile Applications

no code implementations10 Sep 2018 Ji Wang, Bokai Cao, Philip S. Yu, Lichao Sun, Weidong Bao, Xiaomin Zhu

In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning.

Multi-Round Influence Maximization (Extended Version)

1 code implementation12 Feb 2018 Lichao Sun, Weiran Huang, Philip S. Yu, Wei Chen

In this paper, we study the Multi-Round Influence Maximization (MRIM) problem, where influence propagates in multiple rounds independently from possibly different seed sets, and the goal is to select seeds for each round to maximize the expected number of nodes that are activated in at least one round.

Social and Information Networks

Contaminant Removal for Android Malware Detection Systems

no code implementations7 Nov 2017 Lichao Sun, Xiaokai Wei, Jiawei Zhang, Lifang He, Philip S. Yu, Witawas Srisa-an

The results indicate that once we remove contaminants from the datasets, we can significantly improve both malware detection rate and detection accuracy

Cryptography and Security

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