Search Results for author: Lingjuan Lyu

Found 56 papers, 20 papers with code

A Pathway Towards Responsible AI Generated Content

no code implementations2 Mar 2023 Chen Chen, Jie Fu, Lingjuan Lyu

AI Generated Content (AIGC) has received tremendous attention within the past few years, with content ranging from image, text, to audio, video, etc.


On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space

no code implementations23 Feb 2023 Yuyang Deng, Nidham Gazagnadou, Junyuan Hong, Mehrdad Mahdavi, Lingjuan Lyu

Recent studies demonstrated that the adversarially robust learning under $\ell_\infty$ attack is harder to generalize to different domains than standard domain adaptation.

Domain Generalization Federated Learning

ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms

1 code implementation22 Feb 2023 Minzhou Pan, Yi Zeng, Lingjuan Lyu, Xue Lin, Ruoxi Jia

However, we lack a thorough understanding of the applicability of existing detection methods across a variety of learning settings.

backdoor defense Self-Supervised Learning +1

InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation

no code implementations20 Feb 2023 Jiahua Dong, Yang Cong, Gan Sun, Lixu Wang, Lingjuan Lyu, Jun Li, Ender Konukoglu

Moreover, they cannot explore which 3D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3D objects.

3D Object Recognition Fairness

Delving into the Adversarial Robustness of Federated Learning

no code implementations19 Feb 2023 Jie Zhang, Bo Li, Chen Chen, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chao Wu

In this work, we propose a novel algorithm called Decision Boundary based Federated Adversarial Training (DBFAT), which consists of two components (local re-weighting and global regularization) to improve both accuracy and robustness of FL systems.

Adversarial Robustness Federated Learning

GAIN: Enhancing Byzantine Robustness in Federated Learning with Gradient Decomposition

no code implementations13 Feb 2023 Yuchen Liu, Chen Chen, Lingjuan Lyu, Fangzhao Wu, Sai Wu, Gang Chen

Meanwhile, we observe that most existing robust AGgregation Rules (AGRs) fail to stop the aggregated gradient deviating from the optimal gradient (the average of honest gradients) in the non-IID setting.

Federated Learning

SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention

no code implementations27 Jan 2023 Xiaolong Xu, Lingjuan Lyu, Yihong Dong, Yicheng Lu, Weiqiang Wang, Hong Jin

With the frequent happening of privacy leakage and the enactment of privacy laws across different countries, data owners are reluctant to directly share their raw data and labels with any other party.

Classification Federated Learning +1

DEJA VU: Continual Model Generalization For Unseen Domains

2 code implementations25 Jan 2023 Chenxi Liu, Lixu Wang, Lingjuan Lyu, Chen Sun, Xiao Wang, Qi Zhu

To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains.

Data Augmentation Domain Generalization

FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation

1 code implementation14 Dec 2022 Ziqing Fan, Yanfeng Wang, Jiangchao Yao, Lingjuan Lyu, Ya zhang, Qi Tian

However, in addition to previous explorations for improvement in federated averaging, our analysis shows that another critical bottleneck is the poorer optima of client models in more heterogeneous conditions.

Federated Learning

ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals

no code implementations11 Dec 2022 Rui Song, Liguo Zhou, Lingjuan Lyu, Andreas Festag, Alois Knoll

To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training.

Federated Learning Quantization

GNN-SL: Sequence Labeling Based on Nearest Examples via GNN

1 code implementation5 Dec 2022 Shuhe Wang, Yuxian Meng, Rongbin Ouyang, Jiwei Li, Tianwei Zhang, Lingjuan Lyu, Guoyin Wang

To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL model output with similar tagging examples retrieved from the whole training set.

Chinese Word Segmentation named-entity-recognition +4

Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling

no code implementations23 Oct 2022 Junyuan Hong, Lingjuan Lyu, Jiayu Zhou, Michael Spranger

As deep learning blooms with growing demand for computation and data resources, outsourcing model training to a powerful cloud server becomes an attractive alternative to training at a low-power and cost-effective end device.

Model Compression

Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

1 code implementation18 Oct 2022 Zhiyuan Zhang, Lingjuan Lyu, Xingjun Ma, Chenguang Wang, Xu sun

In this work, we take the first step to exploit the pre-trained (unfine-tuned) weights to mitigate backdoors in fine-tuned language models.

Language Modelling Sentiment Analysis +2

Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees

no code implementations4 Sep 2022 Jiaqian Ren, Lei Jiang, Hao Peng, Lingjuan Lyu, Zhiwei Liu, Chaochao Chen, Jia Wu, Xu Bai, Philip S. Yu

Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.

Link Prediction Network Embedding +1

RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation

no code implementations22 Aug 2022 Qucheng Peng, Zhengming Ding, Lingjuan Lyu, Lichao Sun, Chen Chen

For the input-level, we design a new data augmentation technique as Phase MixUp, which highlights task-relevant objects in the interpolations, thus enhancing input-level regularization and class consistency for target models.

Data Augmentation Self-Knowledge Distillation +1

Accelerated Federated Learning with Decoupled Adaptive Optimization

no code implementations14 Jul 2022 Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, Dejing Dou

The federated learning (FL) framework enables edge clients to collaboratively learn a shared inference model while keeping privacy of training data on clients.

Federated Learning

Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion

no code implementations14 Jun 2022 Si Chen, Yi Zeng, Jiachen T. Wang, Won Park, Xun Chen, Lingjuan Lyu, Zhuoqing Mao, Ruoxi Jia

Our work is the first to provide a thorough understanding of leveraging model inversion for effective backdoor removal by addressing key questions about reconstructed samples' properties, perceptual similarity, and the potential presence of backdoor triggers.

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

1 code implementation7 Jun 2022 Tao Qi, Fangzhao Wu, Chuhan Wu, Lingjuan Lyu, Tong Xu, Zhongliang Yang, Yongfeng Huang, Xing Xie

In order to learn a fair unified representation, we send it to each platform storing fairness-sensitive features and apply adversarial learning to remove bias from the unified representation inherited from the biased data.

Fairness Federated Learning +1

Privacy for Free: How does Dataset Condensation Help Privacy?

1 code implementation1 Jun 2022 Tian Dong, Bo Zhao, Lingjuan Lyu

In this work, we for the first time identify that dataset condensation (DC) which is originally designed for improving training efficiency is also a better solution to replace the traditional data generators for private data generation, thus providing privacy for free.

Dataset Condensation Privacy Preserving

CalFAT: Calibrated Federated Adversarial Training with Label Skewness

1 code implementation30 May 2022 Chen Chen, Yuchen Liu, Xingjun Ma, Lingjuan Lyu

In this paper, we study the problem of FAT under label skewness, and reveal one root cause of the training instability and natural accuracy degradation issues: skewed labels lead to non-identical class probabilities and heterogeneous local models.

Adversarial Robustness Federated Learning

QEKD: Query-Efficient and Data-Free Knowledge Distillation from Black-box Models

no code implementations23 May 2022 Jie Zhang, Chen Chen, Jiahua Dong, Ruoxi Jia, Lingjuan Lyu

Knowledge distillation (KD) is a typical method for training a lightweight student model with the help of a well-trained teacher model.

Knowledge Distillation

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

PrivateRec: Differentially Private Training and Serving for Federated News Recommendation

no code implementations18 Apr 2022 Ruixuan Liu, Yanlin Wang, Yang Cao, Lingjuan Lyu, Weike Pan, Yun Chen, Hong Chen

Collecting and training over sensitive personal data raise severe privacy concerns in personalized recommendation systems, and federated learning can potentially alleviate the problem by training models over decentralized user data. However, a theoretically private solution in both the training and serving stages of federated recommendation is essential but still lacking. Furthermore, naively applying differential privacy (DP) to the two stages in federated recommendation would fail to achieve a satisfactory trade-off between privacy and utility due to the high-dimensional characteristics of model gradients and hidden representations. In this work, we propose a federated news recommendation method for achieving a better utility in model training and online serving under a DP guarantee. We first clarify the DP definition over behavior data for each round in the life-circle of federated recommendation systems. Next, we propose a privacy-preserving online serving mechanism under this definition based on the idea of decomposing user embeddings with public basic vectors and perturbing the lower-dimensional combination coefficients.

Federated Learning News Recommendation +2

Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information

2 code implementations11 Apr 2022 Yi Zeng, Minzhou Pan, Hoang Anh Just, Lingjuan Lyu, Meikang Qiu, Ruoxi Jia

With poisoning equal to or less than 0. 5% of the target-class data and 0. 05% of the training set, we can train a model to classify test examples from arbitrary classes into the target class when the examples are patched with a backdoor trigger.

Backdoor Attack Clean-label Backdoor Attack (0.024%) +1

No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices

no code implementations16 Feb 2022 Ruixuan Liu, Fangzhao Wu, Chuhan Wu, Yanlin Wang, Lingjuan Lyu, Hong Chen, Xing Xie

In this way, all the clients can participate in the model learning in FL, and the final model can be big and powerful enough.

Federated Learning Knowledge Distillation +1

Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

no code implementations NeurIPS 2021 Jamie Cui, Chaochao Chen, Lingjuan Lyu, Carl Yang, Li Wang

As a result, our model can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms.

Information Retrieval Retrieval

Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation

no code implementations10 Feb 2022 Chaochao Chen, Huiwen Wu, Jiajie Su, Lingjuan Lyu, Xiaolin Zheng, Li Wang

To this end, PriCDR can not only protect the data privacy of the source domain, but also alleviate the data sparsity of the source domain.

Privacy Preserving Recommendation Systems +1

DENSE: Data-Free One-Shot Federated Learning

1 code implementation23 Dec 2021 Jie Zhang, Chen Chen, Bo Li, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chunhua Shen, Chao Wu

One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round.

Federated Learning

Protecting Intellectual Property of Language Generation APIs with Lexical Watermark

1 code implementation5 Dec 2021 Xuanli He, Qiongkai Xu, Lingjuan Lyu, Fangzhao Wu, Chenguang Wang

Nowadays, due to the breakthrough in natural language generation (NLG), including machine translation, document summarization, image captioning, etc NLG models have been encapsulated in cloud APIs to serve over half a billion people worldwide and process over one hundred billion word generations per day.

Document Summarization Image Captioning +3

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

no code implementations NeurIPS 2021 Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low

In this paper, we adopt federated learning as a gradient-based formalization of collaborative machine learning, propose a novel cosine gradient Shapley value to evaluate the agents’ uploaded model parameter updates/gradients, and design theoretically guaranteed fair rewards in the form of better model performance.

BIG-bench Machine Learning Fairness +1

Anti-Backdoor Learning: Training Clean Models on Poisoned Data

1 code implementation NeurIPS 2021 Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, Xingjun Ma

From this view, we identify two inherent characteristics of backdoor attacks as their weaknesses: 1) the models learn backdoored data much faster than learning with clean data, and the stronger the attack the faster the model converges on backdoored data; 2) the backdoor task is tied to a specific class (the backdoor target class).

Backdoor 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.

FedKD: Communication Efficient Federated Learning via Knowledge Distillation

no code implementations30 Aug 2021 Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, Xing Xie

Instead of directly communicating the large models between clients and server, we propose an adaptive mutual distillation framework to reciprocally learn a student and a teacher model on each client, where only the student model is shared by different clients and updated collaboratively to reduce the communication cost.

Federated Learning Knowledge Distillation

A Novel Attribute Reconstruction Attack in Federated Learning

no code implementations16 Aug 2021 Lingjuan Lyu, Chen Chen

We perform the first systematic evaluation of attribute reconstruction attack (ARA) launched by the malicious server in the FL system, and empirically demonstrate that the shared epoch-averaged local model gradients can reveal sensitive attributes of local training data of any victim participant.

Federated Learning

A Vertical Federated Learning Framework for Graph Convolutional Network

no code implementations22 Jun 2021 Xiang Ni, Xiaolong Xu, Lingjuan Lyu, Changhua Meng, Weiqiang Wang

Recently, Graph Neural Network (GNN) has achieved remarkable success in various real-world problems on graph data.

Federated Learning Node Classification +1

Defending Against Backdoor Attacks in Natural Language Generation

1 code implementation3 Jun 2021 Xiaofei Sun, Xiaoya Li, Yuxian Meng, Xiang Ao, Lingjuan Lyu, Jiwei Li, Tianwei Zhang

The frustratingly fragile nature of neural network models make current natural language generation (NLG) systems prone to backdoor attacks and generate malicious sequences that could be sexist or offensive.

Backdoor Attack Dialogue Generation +2

Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs

no code implementations23 May 2021 Chen Chen, Xuanli He, Lingjuan Lyu, Fangzhao Wu

In this work, we bridge this gap by first presenting an effective model extraction attack, where the adversary can practically steal a BERT-based API (the target/victim model) by only querying a limited number of queries.

Inference Attack Model extraction +3

Robust Training Using Natural Transformation

no code implementations10 May 2021 Shuo Wang, Lingjuan Lyu, Surya Nepal, Carsten Rudolph, Marthie Grobler, Kristen Moore

We target attributes of the input images that are independent of the class identification, and manipulate those attributes to mimic real-world natural transformations (NaTra) of the inputs, which are then used to augment the training dataset of the image classifier.

Data Augmentation Image Classification +1

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 +2

Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks

1 code implementation ICLR 2021 Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, Xingjun Ma

NAD utilizes a teacher network to guide the finetuning of the backdoored student network on a small clean subset of data such that the intermediate-layer attention of the student network aligns with that of the teacher network.


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 +3

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 Privacy Preserving

A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning

2 code implementations20 Nov 2020 Xinyi Xu, Lingjuan Lyu

In this paper, we propose a novel Robust and Fair Federated Learning (RFFL) framework to achieve collaborative fairness and adversarial robustness simultaneously via a reputation mechanism.

Adversarial Defense Adversarial Robustness +2

Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

2 code implementations Findings of the Association for Computational Linguistics 2020 Lingjuan Lyu, Xuanli He, Yitong Li

It has been demonstrated that hidden representation learned by a deep model can encode private information of the input, hence can be exploited to recover such information with reasonable accuracy.


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

Collaborative Fairness in Federated Learning

1 code implementation27 Aug 2020 Lingjuan Lyu, Xinyi Xu, Qian Wang

In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability.

Fairness Federated Learning

Local Differential Privacy and Its Applications: A Comprehensive Survey

no code implementations9 Aug 2020 Mengmeng Yang, Lingjuan Lyu, Jun Zhao, Tianqing Zhu, Kwok-Yan Lam

Local differential privacy (LDP), as a strong privacy tool, has been widely deployed in the real world in recent years.

Cryptography and Security

Towards Differentially Private Text Representations

no code implementations25 Jun 2020 Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao

Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model.

Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification

no code implementations25 May 2020 Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.

Classification General Classification +2

Local Differential Privacy based Federated Learning for Internet of Things

no code implementations19 Apr 2020 Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusit Niyato, Kwok-Yan Lam

To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.

BIG-bench Machine Learning Federated Learning +1

Threats to Federated Learning: A Survey

no code implementations4 Mar 2020 Lingjuan Lyu, Han Yu, Qiang Yang

It is thus of paramount importance to make FL system designers to be aware of the implications of future FL algorithm design on privacy-preservation.

Federated Learning

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

no code implementations26 Jun 2019 Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, Yingbo Liu

To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data.

Edge-computing Federated Learning +1

Towards Fair and Privacy-Preserving Federated Deep Models

1 code implementation4 Jun 2019 Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu, Kee Siong Ng

This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates.

Benchmarking Fairness +3

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