Search Results for author: Huili Chen

Found 17 papers, 2 papers with code

ActPerFL: Active Personalized Federated Learning

no code implementations FL4NLP (ACL) 2022 Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang

Inspired by Bayesian hierarchical models, we develop ActPerFL, a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients’ training.

Personalized Federated Learning Uncertainty Quantification

Comparative Analysis of Deep Learning Models for Brand Logo Classification in Real-World Scenarios

no code implementations20 May 2023 Qimao Yang, Huili Chen, Qiwei Dong

This report presents a comprehensive study on deep learning models for brand logo classification in real-world scenarios.

Classification

Joint Engagement Classification using Video Augmentation Techniques for Multi-person Human-robot Interaction

no code implementations28 Dec 2022 Yubin Kim, Huili Chen, Sharifa Alghowinem, Cynthia Breazeal, Hae Won Park

This work serves as the first step toward fully unlocking the potential of end-to-end video understanding models pre-trained on large public datasets and augmented with data augmentation and visualization techniques for affect recognition in the multi-person human-robot interaction in the wild.

Data Augmentation Face Swapping +1

Self-Aware Personalized Federated Learning

no code implementations17 Apr 2022 Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang

In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned.

Personalized Federated Learning Uncertainty Quantification

AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection

no code implementations12 Apr 2022 Huili Chen, Xinqiao Zhang, Ke Huang, Farinaz Koushanfar

This paper proposes AdaTest, a novel adaptive test pattern generation framework for efficient and reliable Hardware Trojan (HT) detection.

Backdoor Attack Reinforcement Learning (RL)

An Adaptive Black-box Backdoor Detection Method for Deep Neural Networks

1 code implementation8 Apr 2022 Xinqiao Zhang, Huili Chen, Ke Huang, Farinaz Koushanfar

Deep Neural Networks (DNNs) have demonstrated unprecedented performance across various fields such as medical diagnosis and autonomous driving.

Autonomous Driving Medical Diagnosis

Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection

no code implementations21 Feb 2022 Yein Kim, Huili Chen, Farinaz Koushanfar

The goal of federated learning (FL) is to train one global model by aggregating model parameters updated independently on edge devices without accessing users' private data.

backdoor defense Federated Learning +1

TAD: Trigger Approximation based Black-box Trojan Detection for AI

no code implementations3 Feb 2021 Xinqiao Zhang, Huili Chen, Farinaz Koushanfar

While DNNs are widely employed in security-sensitive fields, they are identified to be vulnerable to Neural Trojan (NT) attacks that are controlled and activated by the stealthy trigger.

Autonomous Driving Medical Diagnosis

ProFlip: Targeted Trojan Attack With Progressive Bit Flips

no code implementations ICCV 2021 Huili Chen, Cheng Fu, Jishen Zhao, Farinaz Koushanfar

In this work, we present ProFlip, the first targeted Trojan attack framework that can divert the prediction of the DNN to the target class by progressively identifying and flipping a small set of bits in model parameters.

Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset

no code implementations20 Aug 2020 Huili Chen, Yue Zhang, Felix Weninger, Rosalind Picard, Cynthia Breazeal, Hae Won Park

Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing.

A Neural-based Program Decompiler

no code implementations28 Jun 2019 Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao

Reverse engineering of binary executables is a critical problem in the computer security domain.

Computer Security Malware Detection

BlackMarks: Black-box Multi-bit Watermarking for Deep Neural Networks

no code implementations ICLR 2019 Huili Chen, Bita Darvish Rouhani, Farinaz Koushanfar

To extract the WM, BlackMarks queries the model with the WM key images and decodes the owner’s signature from the corresponding predictions using the designed encoding scheme.

DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models

2 code implementations2 Apr 2018 Bita Darvish Rouhani, Huili Chen, Farinaz Koushanfar

The resulting models are therefore considered to be the IP of the model builder and need to be protected to preserve the owner's competitive advantage.

Cryptography and Security

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