research
∙
08/16/2023
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
This paper focuses on addressing the practical yet challenging problem o...
share
research
∙
07/21/2023
MAS: Towards Resource-Efficient Federated Multiple-Task Learning
Federated learning (FL) is an emerging distributed machine learning meth...
research
∙
07/07/2023
Federated Learning over a Wireless Network: Distributed User Selection through Random Access
User selection has become crucial for decreasing the communication costs...
research
∙
06/25/2023
FedSampling: A Better Sampling Strategy for Federated Learning
Federated learning (FL) is an important technique for learning models fr...
research
∙
06/16/2023
Pushing the Limits of ChatGPT on NLP Tasks
Despite the success of ChatGPT, its performances on most NLP tasks are s...
research
∙
06/09/2023
Is Normalization Indispensable for Multi-domain Federated Learning?
Federated learning (FL) enhances data privacy with collaborative in-situ...
research
∙
06/04/2023
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Data-free knowledge distillation (KD) helps transfer knowledge from a pr...
research
∙
05/29/2023
Reducing Communication for Split Learning by Randomized Top-k Sparsification
Split learning is a simple solution for Vertical Federated Learning (VFL...
research
∙
05/24/2023
Reconstructive Neuron Pruning for Backdoor Defense
Deep neural networks (DNNs) have been found to be vulnerable to backdoor...
research
∙
05/19/2023
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection
Machine learning (ML) has revolutionized transportation systems, enablin...
research
∙
05/17/2023
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark
Large language models (LLMs) have demonstrated powerful capabilities in ...
research
∙
04/19/2023
DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation
Visual surveillance technology is an indispensable functional component ...
research
∙
03/25/2023
Backdoor Attacks with Input-unique Triggers in NLP
Backdoor attack aims at inducing neural models to make incorrect predict...
research
∙
02/23/2023
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space
Recent studies demonstrated that the adversarially robust learning under...
research
∙
02/22/2023
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms
Backdoor data detection is traditionally studied in an end-to-end superv...
research
∙
02/20/2023
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation
3D object recognition has successfully become an appealing research topi...
research
∙
01/27/2023
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention
With the frequent happening of privacy leakage and the enactment of priv...
research
∙
01/25/2023
DEJA VU: Continual Model Generalization For Unseen Domains
In real-world applications, deep learning models often run in non-statio...
research
∙
12/14/2022
FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation
The statistical heterogeneity of the non-independent and identically dis...
research
∙
12/11/2022
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals
Federated learning enables cooperative training among massively distribu...
research
∙
12/05/2022
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
To better handle long-tail cases in the sequence labeling (SL) task, in ...
research
∙
11/01/2022
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning
Semi-supervised machine learning (SSL) is gaining popularity as it reduc...
research
∙
10/23/2022
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling
As deep learning blooms with growing demand for computation and data res...
research
∙
10/18/2022
Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models
Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attac...
research
∙
10/12/2022
How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?
Given the volume of data needed to train modern machine learning models,...
research
∙
09/19/2022
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks
Previous works have validated that text generation APIs can be stolen th...
research
∙
09/04/2022
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Integrating multiple online social networks (OSNs) has important implica...
research
∙
07/14/2022
Accelerated Federated Learning with Decoupled Adaptive Optimization
The federated learning (FL) framework enables edge clients to collaborat...
research
∙
06/07/2022
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Vertical federated learning (VFL) is a privacy-preserving machine learni...
research
∙
06/01/2022
Privacy for Free: How does Dataset Condensation Help Privacy?
To prevent unintentional data leakage, research community has resorted t...
research
∙
05/08/2022
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks
Graph neural networks (GNNs) have been widely used in modeling graph str...
research
∙
04/18/2022
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation
Privacy protection is an essential issue in personalized news recommenda...
research
∙
04/11/2022
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information
Backdoor attacks insert malicious data into a training set so that, duri...
research
∙
04/02/2022
Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer
With the rapid development of cloud manufacturing, industrial production...
research
∙
02/16/2022
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
Federated learning (FL) is an important paradigm for training global mod...
research
∙
02/15/2022
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
Social recommendation has shown promising improvements over traditional ...
research
∙
02/14/2022
Threats to Pre-trained Language Models: Survey and Taxonomy
Pre-trained language models (PTLMs) have achieved great success and rema...
research
∙
02/10/2022
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
Cross Domain Recommendation (CDR) has been popularly studied to alleviat...
research
∙
12/05/2021
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
Nowadays, due to the breakthrough in natural language generation (NLG), ...
research
∙
10/22/2021
Anti-Backdoor Learning: Training Clean Models on Poisoned Data
Backdoor attack has emerged as a major security threat to deep neural ne...
research
∙
09/03/2021
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data
Since training a large-scale backdoored model from scratch requires a la...
research
∙
08/30/2021
FedKD: Communication Efficient Federated Learning via Knowledge Distillation
Federated learning is widely used to learn intelligent models from decen...
research
∙
08/29/2021
Beyond Model Extraction: Imitation Attack for Black-Box NLP APIs
Machine-learning-as-a-service (MLaaS) has attracted millions of users to...
research
∙
06/22/2021
A Vertical Federated Learning Framework for Graph Convolutional Network
Recently, Graph Neural Network (GNN) has achieved remarkable success in ...
research
∙
05/23/2021
Killing Two Birds with One Stone: Stealing Model and Inferring Attribute from BERT-based APIs
The advances in pre-trained models (e.g., BERT, XLNET and etc) have larg...
research
∙
05/11/2021
DP-SIGNSGD: When Efficiency Meets Privacy and Robustness
Federated learning (FL) has emerged as a promising collaboration paradig...
research
∙
05/10/2021
Robust Training Using Natural Transformation
Previous robustness approaches for deep learning models such as data aug...
research
∙
03/18/2021
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!
Natural language processing (NLP) tasks, ranging from text classificatio...
research
∙
01/15/2021
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a ...
research
∙
12/07/2020
