Huynh Thi Thanh Binh

Bio: Huynh Thi Thanh Binh is an academic researcher from Hanoi University of Science and Technology. The author has contributed to research in topics: Wireless sensor network & Evolutionary algorithm. The author has an hindex of 14, co-authored 110 publications receiving 742 citations. Previous affiliations of Huynh Thi Thanh Binh include Hanoi University & Vietnam National University, Ho Chi Minh City.

PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems

Abstract: Edge computing is a key-enabling technology that meets continuously increasing requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the increasing privacy leakages of machine learning while benefiting from unbalanced data distributions, federated learning has been wildly adopted as a novel intelligent edge computing framework with a localized training mechanism. However, recent studies found that the federated learning framework exhibits inherent vulnerabilities on active attacks, and poisoning attack is one of the most powerful and secluded attacks where the functionalities of the global model could be damaged through attacker’s well-crafted local updates. In this article, we give a comprehensive exploration of the poisoning attack mechanisms in the context of federated learning. We first present a poison data generation method, named Data_Gen , based on the generative adversarial networks (GANs). This method mainly relies upon the iteratively updated global model parameters to regenerate samples of interested victims. Second, we further propose a novel generative poisoning attack model, named PoisonGAN , against the federated learning framework. This model utilizes the designed Data_Gen method to efficiently reduce the attack assumptions and make attacks feasible in practice. We finally evaluate our data generation and attack models by implementing two types of typical poisoning attack strategies, label flipping and backdoor, on a federated learning prototype. The experimental results demonstrate that these two attack models are effective in federated learning.

Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks

Abstract: The popularity of Wireless Sensor Networks (WSNs) is rapidly growing due to its wide-ranged applications such as industrial diagnostics, environment monitoring or surveillance. High-quality construction of WSNs is increasingly demanding due to the ubiquity of WSNs. The current work is focused on improving one of the most crucial criteria that appear to exert an enormous impact on the WSNs performance, namely the area coverage. The proposed model is involved with sensor nodes deployment which maximizes the area coverage. This problem is proved to be NP-hard. Although such algorithms to handle this problem with fairly acceptable solutions had been introduced, most of them still heavily suffer from several issues including the large computation time and solution instability. Hence, the existing work proposed ways to overcome such difficulties by proposing two nature-based algorithms, namely Improved Cuckoo Search (ICS) and Chaotic Flower Pollination algorithm (CFPA). By adopting the concept of calculating the adaptability and a well-designed local search in previous studies, those two algorithms are able to improve their performance. The experimental results on 15 instances established a huge enhancement in terms of computation time, solution quality and stability.

Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud–Fog Computing Environment

Abstract: In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Deep Learning in Mobile and Wireless Networking: A Survey

Abstract: The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

Abstract: This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation. Our contributions are the following: (1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; (2) we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; (3) we perform data fusion from heterogeneous sensors (optical and laser) using residual correction. Our framework improves state-of-the-art accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.