b'Mung Chiang'

research

∙ 05/22/2023

Asynchronous Multi-Model Federated Learning over Wireless Networks: Theory, Modeling, and Optimization

Federated learning (FL) has emerged as a key technique for distributed m...

0 Zhan-Lun Chang, et al. ∙

research

∙ 03/15/2023

Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networks

Semi-decentralized federated learning blends the conventional device to-...

0 Rohit Parasnis, et al. ∙

research

∙ 03/15/2023

Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks

Federated learning (FL) has been promoted as a popular technique for tra...

0 Su Wang, et al. ∙

research

∙ 08/17/2022

Interference Cancellation GAN Framework for Dynamic Channels

Symbol detection is a fundamental and challenging problem in modern comm...

9 Hung T. Nguyen, et al. ∙

research

∙ 08/04/2022

Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange

Federated learning (FL) has been recognized as one of the most promising...

3 Satyavrat Wagle, et al. ∙

research

∙ 03/26/2022

Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point

We propose cooperative edge-assisted dynamic federated learning (CE-FL)....

0 Bhargav Ganguly, et al. ∙

research

∙ 03/23/2022

Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing

Federated learning is a prime candidate for distributed machine learning...

3 Hung T. Nguyen, et al. ∙

research

∙ 02/07/2022

Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks

Federated learning (FedL) has emerged as a popular technique for distrib...

3 Seyyedali Hosseinalipour, et al. ∙

research

∙ 12/21/2021

Adversarial Neural Networks for Error Correcting Codes

Error correcting codes are a fundamental component in modern day communi...

11 Hung T. Nguyen, et al. ∙

research

∙ 12/21/2021

On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge

Edge computing has revolutionized the world of mobile and wireless netwo...

6 Hung T. Nguyen, et al. ∙

research

∙ 06/29/2021

UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach

We consider distributed machine learning (ML) through unmanned aerial ve...

33 Su Wang, et al. ∙

research

∙ 04/19/2021

Improving Adversarial Robustness Using Proxy Distributions

We focus on the use of proxy distributions, i.e., approximations of the ...

9 Vikash Sehwag, et al. ∙

research

∙ 03/22/2021

SSD: A Unified Framework for Self-Supervised Outlier Detection

We ask the following question: what training information is required to ...

18 Vikash Sehwag, et al. ∙

research

∙ 01/04/2021

Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation

The conventional federated learning (FedL) architecture distributes mach...

0 Su Wang, et al. ∙

research

∙ 11/11/2020

I-BOT: Interference-Based Orchestration of Tasks for Dynamic Unmanaged Edge Computing

In recent years, edge computing has become a popular choice for latency-...

0 Shikhar Suryavansh, et al. ∙

research

∙ 10/19/2020

RobustBench: a standardized adversarial robustness benchmark

Evaluation of adversarial robustness is often error-prone leading to ove...

4 Francesco Croce, et al. ∙

research

∙ 07/29/2020

Paid Prioritization with Content Competition

We study the effects of allowing paid prioritization arrangements in a m...

0 Parinaz Naghizadeh, et al. ∙

research

∙ 07/26/2020

Fast-Convergent Federated Learning

Federated learning has emerged recently as a promising solution for dist...

0 Hung T. Nguyen, et al. ∙

research

∙ 06/24/2020

Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks

With increasing expressive power, deep neural networks have significantl...

0 Vikash Sehwag, et al. ∙

research

∙ 06/07/2020

From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks

Contemporary network architectures are pushing computing tasks from the ...

0 Seyyedali Hosseinalipour, et al. ∙

research

∙ 04/20/2020

Securing Internet Applications from Routing Attacks

Attacks on Internet routing are typically viewed through the lens of ava...

0 Yixin Sun, et al. ∙

research

∙ 12/20/2019

Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap

Cyberphysical systems (CPS) are ubiquitous in our personal and professio...

0 Somali Chaterji, et al. ∙

research

∙ 12/16/2019

AppStreamer: Reducing Storage Requirements of Mobile Games through Predictive Streaming

Storage has become a constrained resource on smartphones. Gaming is a po...

0 Nawanol Theera-Ampornpunt, et al. ∙

research

∙ 05/05/2019

Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples

A large body of recent work has investigated the phenomenon of evasion a...

18 Vikash Sehwag, et al. ∙

research

∙ 11/06/2018

Characterizing Task Completion Latencies in Fog Computing

Fog computing, which distributes computing resources to multiple locatio...

0 Maria Gorlatova, et al. ∙

research

∙ 06/22/2018

Personalized Thread Recommendation for MOOC Discussion Forums

Social learning, i.e., students learning from each other through social ...

0 Andrew S. Lan, et al. ∙

research

∙ 06/09/2018

An Estimation and Analysis Framework for the Rasch Model

The Rasch model is widely used for item response analysis in application...

0 Andrew S. Lan, et al. ∙

research

∙ 02/18/2018

DARTS: Deceiving Autonomous Cars with Toxic Signs

Sign recognition is an integral part of autonomous cars. Any misclassifi...

0 Chawin Sitawarin, et al. ∙

research

∙ 02/11/2018

Stochastic Non-preemptive Co-flow Scheduling with Time-Indexed Relaxation

Co-flows model a modern scheduling setting that is commonly found in a v...

0 Ruijiu Mao, et al. ∙

research

∙ 02/01/2018

Linearized Binary Regression

Probit regression was first proposed by Bliss in 1934 to study mortality...

0 Andrew S. Lan, et al. ∙

research

∙ 01/09/2018

Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos

We propose a new real-world attack against the computer vision based sys...

0 Chawin Sitawarin, et al. ∙

research

∙ 01/05/2018

Tempest: Temporal Dynamics in Anonymity Systems

Many recent proposals for anonymous communication omit from their securi...

0 Ryan Wails, et al. ∙

research

∙ 12/21/2017

Acoustic Denial of Service Attacks on HDDs

Among storage components, hard disk drives (HDDs) have become the most c...

0 Mohammad Shahrad, et al. ∙

research

∙ 11/30/2017

Virtualized Control over Fog: Interplay Between Reliability and Latency

This paper introduces an analytical framework to investigate optimal des...

0 Hazer Inaltekin, et al. ∙

Mung Chiang

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