Graph neural networks (GNNs) are effective for graph-based node classification tasks, such as data mining and recommendation systems. Combining federated learning(FL) with GNN enables multiple participants to collaboratively train powerful models without sharing private data. However, subgraph-level FL faces challenges, including missing cross-client edges and non-IID data distributions. Additionally

With the rapid development of information technology, efficient energy utilization has become a major challenge in modern computing system design. This paper focuses on the energy-constrained parallel application scheduling problem in heterogeneous systems and proposes three algorithms to minimize the makespan of applications. The first one is the minimum makespan algorithm under energy constraints

Object tracking technology plays a critical role in analysing population flow in high-traffic areas like road intersections. While existing multiple-object tracking (MOT) methods have set benchmarks for accuracy and speed, they often face challenges with real-time processing in densely populated scenes, where the sheer number of objects and frequent occlusions make tracking difficult. This challenge

Modern real-time systems utilize considerable amounts of power while executing computation-intensive tasks. The execution of these tasks leads to significant power dissipation and heating of the device. It therefore results in severe thermal issues like temperature escalation, high thermal gradients, and excessive hot spot formation, which may result in degrading chip performance, accelerating device

Computational workflow management systems power contemporary data-intensive sciences. The slowly resolving reproducibility crisis presents both a sobering warning and an opportunity to iterate on what science and data processing entails. The Square Kilometre Array (SKA), the world’s largest radio telescope, is among the most extensive scientific projects underway and presents grand scientific collaboration

Traditional research on vehicular edge computing often overlooks the need for large amounts of real-time data with temporal and spatial characteristics. Existing task offloading strategies mainly use a binary approach, neglecting the limited vehicle computing resources and failing to utilize vehicle and edge server resources fully. To this end, this paper proposes a spatio-temporal prediction and Deep

In recent years, the progress in large language models has made automatic vulnerability repair a viable solution. Security researchers have proposed a series of Automated Vulnerability Repair (AVR) methods. However, for AVR models to be effective, precise identification of the vulnerability trigger points (i.e., the exact lines of code where the vulnerability resides) is essential. Although current

Pattern analysis is responsible for a significant role in data extraction as we enter the era of big data, providing valuable information. Regular patterns, which are temporally consistent patterns in transactional data, offer significant and intelligent insights in various areas. Temporal regularity in a regular pattern allows analyzing and recognizing noteworthy knowledge that appear recurrently

In industry 4.0, Industrial Cyber–Physical Systems (ICPS) integrate industrial machines with computer control and data analysis. Federated Learning (FL) improves this by enabling collaborative machine learning and improvement while maintaining data privacy. This method improves the security, and intelligence of industrial processes. FL-based frameworks proposed in the literature do not perform rigorous

Data-driven fully-supervised online action detection algorithms heavily rely on manual annotations, which are challenging to obtain in real-world applications. Current research efforts aim to address this issue by introducing weakly supervised online action detection (WOAD) methods that utilize video-level annotations. However, these approaches frequently face challenges with blurred temporal boundaries

Skewed data distribution leads to certain tasks or nodes handling much more data than others, thereby slowing down their execution speed and classifying them as stragglers. Existing solutions attempt to establish a well-balanced workload to mitigate stragglers by using either data stream grouping or task scheduling. This “one size fits all” approach only considers single-level requirements and fails

E-business is evolving towards the creation of a global network of interconnected smart devices, aimed at enhancing a wide array of applications through their ability to sense, connect, and analyze data. At the heart of this evolution, the Industrial Internet of Things (IIoT) emerges as a pivotal element in the era of ‘Industry 4.0.’ This paper proposes a novel framework that integrates fog/edge computing

Split Federated Learning (SFL) enables organizations such as healthcare to collaborate to improve model performance without sharing private data. However, SFL is currently susceptible to model inversion (MI) attacks, which create a serious problem of risk for private data leakage and loss of accuracy. Therefore, this paper proposes an innovative framework called Knowledge Distillation Resistance Transfer

Facing the computational challenges in mobile devices within blockchain networks, particularly the scarcity and underutilization of computational resources, this paper introduces the CAGE Framework: a novel architecture based on cooperative game theory within alliance blockchains. Designed to optimize computational resource allocation across multiple mobile terminals, CAGE Framework leverages a tri-layer

Quantization plays a crucial role in efficiently deploying deep learning models on resources constraint devices. Post-training quantization does not require either access to the original dataset or retraining the full model. Current methods that achieve high performance (near baseline results) require INT8 fixed-point integers. However, to achieve high model compression by achieving lower bit-width

Federated Learning (FL) recently emerged as a practical privacy-preserving paradigm to exploit data distributed over separated repositories for Machine Learning purposes, with no need to migrate data. FL algorithms entail concerted activities of multiple distributed players: a dedicated supporting system aims to relieve programmers from dealing with the intricate implementation details of communication

The rapid growth of Internet of Things (IoT) applications depends on the deployment of numerous heterogeneous devices, and the deployed devices require various communication protocols to be accessed. Matching the correct protocol for accessed devices, particularly those with unknown protocols, is a complex and challenging task due to the diversity of device types, the growing number of protocols, and

Federated Learning (FL) is an emerging training framework for machine learning to protect data privacy without accessing the original data from each client. However, the participating clients have different computing resources in FL. Clients with insufficient resources may not cooperate in the training due to hardware limitations. The restricted computing speeds may also slow down the overall computing

Federated Learning is a collaborative machine learning (ML) framework designed to train a globally shared model without accessing participants’ private data. However, due to the statistical heterogeneity in the participants’ data, federated learning faces significant challenges. This approach generates a similar output for all participants, without adapting the model to each individual. Consequently

The advent of quantum computers has justified the development of quantum machine learning algorithms, based on the adaptation of the principles of machine learning to the formalism of qubits. Among such quantum algorithms, anomaly detection represents an important problem crossing several disciplines from cybersecurity, to fraud detection to particle physics. We summarize the key concepts involved

Low Earth Orbit (LEO) satellites have been widely used to collect sensing data from ground-based IoT devices. Comprehensive and timely collection of sensor data is a prerequisite for conducting analysis, decision-making, and other tasks, ultimately enhancing services such as geological hazard monitoring and ecological environment monitoring. To improve the efficiency of data collection, many models

Decentralized Finance (DeFi) utilizes the key principles of blockchain to improve the traditional finance system with greater freedom in trade. However, due to the absence of access restrictions in the implementation of decentralized finance protocols, effective regulatory measures are crucial to ensuring the healthy development of DeFi ecosystems. As a prominent DeFi platform, Ethereum has witnessed

Owing to the widespread application of artificial intelligence, deep learning (DL) has attracted considerable attention from both academia and industry. The DL workload-training process is a key step in determining the quality of DL-based applications. However, owing to the limited computational power of conventionally centralized clusters, it is more beneficial to accelerate workload training while

With the emergence of Internet of Things (IoT) devices collecting large amounts of data at the edges of the network, a new generation of hyper-distributed applications is emerging, spanning cloud, fog, and edge computing resources. The automated deployment and management of such applications requires orchestration tools that take a deployment descriptor (e.g. Kubernetes manifest, Helm chart or TOSCA)

A computational workflow, also known as workflow, consists of tasks that are executed in a certain order to attain a specific computational campaign. Computational workflows are commonly employed in science domains, such as physics, chemistry, genomics, to complete large-scale experiments in distributed and heterogeneous computing environments. However, running computations at such a large scale makes

To tackle the challenges posed by Moore’s Law, Chiplet technology emerges as a promising solution. Chiplets comprising CPUs and accelerators are connected by Networks-on-Chip (NoC) for large-scale integration and efficient communications. However, the slow simulation speed of NoCs has become a bottleneck, limiting the overall performance of chiplet simulations. Existing solutions only focus on accelerating

The industrial landscape is undergoing a significant transformation, moving away from traditional wired fieldbus networks to cutting-edge 5G mobile networks. This transition, extending from local applications to company-wide use and spanning multiple factories, is driven by the promise of low-latency communication and seamless connectivity for various devices in industrial settings. However, besides

In the Internet of Vehicles (IoV) based on Cellular Vehicle-to-Everything (C-V2X) wireless communication, vehicles inform surrounding vehicles and infrastructure of their status by broadcasting basic safety messages, enhancing traffic management capabilities. Since anomalous vehicles can broadcast false traffic messages, anomaly detection is crucial for IoV. State-of-the-art methods typically utilize

Remote sensing analysis is a dominant yet time-consuming part of geospatial applications. The performance can be optimized based on distributed computing, but current systems still face significant challenges. Firstly, the spatial characteristics of remote sensing data lead to an uneven distribution of computational intensity (CIT), which characterizes computing loads, including computation and IO

Collaborative data sharing underlies applications in systems such as online social networks and cloud storage. A central provider hosts shared data, e.g., a Facebook group page, and provides sharing users with read/write access according to user-defined settings. Historical incidents prove that data storage centralization enables a corrupted provider to censor or diverge users’ views of the shared

Two primary characteristics of cloud computing are energy consumption and execution time optimization. The massive amount of data that needs to be processed grows as the number of data centers does. One of the most popular strategies for reducing energy waste while making the best use of available resources is the efficient scheduling of user processes carried out in the cloud. In this research, we

Programming language detection from source code excepts remains an active research field, which has already been addressed with machine learning and natural language processing. Identifying the language of short code snippets poses both benefits and challenges across various scenarios, such as embedded code analysis, forums, Q&A systems, search engines, source code repositories, and text editors. Existing

Federated learning (FL), a decentralized machine learning framework that allows edge devices (i.e., clients) to train a global model while preserving data/client privacy, has become increasingly popular recently. In FL, a shared global model is built by aggregating the updated parameters in a distributed manner. To incentivize data owners to participate in FL, it is essential for service providers

Post-quantum computing becomes a real threat in the coming years, resulting in vulnerable security protocols that rely on traditional public key algorithms. It is not evident to provide protection against it in a cost-efficient manner, especially for Internet of Things (IoT) devices with limited capabilities. There is a high variety of IoT applications, some require only short-term security (e.g. agriculture)

The number of Internet of Things (IoT) device connections is increasing rapidly as IoT applications are vital in any operation. IoT must maintain safe internet access that withstands various malicious attacks for instance Recon, Mirai, Distributed Denial of Service (DDoS), and Spoofing which has gained much attention. Intelligently changing and zero-day attacks are emerging every day. This highlights

Edge computing typically handles applications as multiple dependent sub-tasks presented with Directed Acyclic Graphs (DAGs) and offloads them to multiple edge servers to reduce network bandwidth pressure thereby improving task execution efficiency. However, security risk may be increased due to the involvement of many edge servers in a DAG-based task execution. For instance, execution may fail if any

Recent advances in hardware and software technologies have led to the design of many pervasively distributed IoT devices that can generate/consume data and manage multiple sensors and actuators, paving the way for new applications and services. However, these new features, at the same time, can easily become an enticing “grab point” for attackers, unlocking a newer and larger attack space and exposing

The growth of the bid data set has become an integral part of business enterprises. Still, for responding to bids, predicting the making decision is considered as the most important. So, many techniques and algorithms become a crucial solution. In this context, artificial intelligent techniques for prediction are applied to aid in decision making. In this study, we consider machine learning algorithms

Edge Computing (EC) extends computing functionalities from remote cloud data centers to the proximity of end-user devices at the network edges, thereby reducing application response time. However, existing solutions face challenges in scalability, efficient resource management, and near real-time adaptation in dynamic environments. In this paper, we jointly investigate the application placement and

Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which involves placing computer workloads (both hardware and software) as close as possible to the edge, where data are created and where actions are occurring, enabling faster response

The hybrid cloud is a crucial solution to overcome the limited resources of the private cloud and efficiently execute large-scale workflow due to its easy scalability and ability to guarantee data privacy. However, most of the existing studies on multi-objective workflow scheduling in a hybrid cloud treat the problem as a black box and perform overall optimization of large-scale decision variables

Climate change mitigation requires the urgent reduction of Greenhouse Gas (GHG) emissions, with the building sector as a significant contributor. This study develops a system to identify appliance profiles from smart meter data, enhancing energy consumption awareness and management. These profiles provide valuable insights into users’ consumption patterns and habits, enabling more accurate load consumption

The execution of data intensive analysis workflows in a multi-cloud environment, such as the World Large hadron collider Computing Grid (WLCG) at CERN, requires a large amount of input data, which is stored in multiple storage elements. The turnaround time taken by an individual analysis workflow running on an edge machine is mostly affected by the data reading time. Minimizing the data reading time

Energy efficiency of Convolutional Neural Networks (CNNs) has become an important area of research, with various strategies being developed to minimize the power consumption of these models. Previous efforts, including techniques like model pruning, quantization, and hardware optimization, have made significant strides in this direction. However, there remains a need for more effective on device AI

Serverless edge computing provides a lightweight and easily scalable new paradigm for edge computing, which is widespread in many fields. However, its characteristics of fine-grained tasks, short startup times, and fast execution speed bring new challenges in task offloading and latency reduction. In this paper, we consider the task offloading problem of serverless functions in a multi-edge-to-cloud

Data sharing is key to enabling data analysis and research advancement, and that is especially true in healthcare. Due to the inherited sensitivity of health data, institutions are still wary of sharing their data, especially with the increasing number of breaches in recent years and the strict privacy legislation involved (GDPR, HIPAA, etc.). Privacy and security concerns exist when making data available

Multi-access edge computing provides proximate intelligent services for distributed users. Due to the user’s mobility and highly dynamic network, edge servers with limited coverage cannot ensure continuity of running services and maintain high-level Quality of Service. To tackle this issue, an effective service migration strategy is of paramount importance. However, the current approach ignores the

With the development of the Internet of Things (IoT), Federated Learning (FL) is extensively employed in smart cities and industrial IoT, involving numerous heterogeneous devices with varying computational and storage capabilities. Traditional FL assumes that clients have enough resources to train a unified global model from the beginning to the end of training. However, it ignores the problem of uneven

This paper introduces JEMADAR-AI, an approach to energy management within smart grids, leveraging an edge-cloud continuum architecture coupled with Deep Q-Learning to optimize the operation of smart home devices. The main hypothesis of this work is that combining advanced machine learning models with edge-cloud computing can significantly improve energy efficiency and cost savings in smart grids. The

As cloud demand for computation and network resources fluctuates, effective resource management becomes essential for optimizing allocation and enhancing performance in virtualization-based applications. Current methods struggle to efficiently schedule multiple virtual resources for dynamic workloads. To address this, we propose a self-adaptive elastic virtual resource management (EVRM) framework that

The separation of data ownership and management rights in cloud storage architectures results in losing control over outsourced data, making it challenging to achieve deterministic deletion and verify-deletion results. This predicament precipitates security vulnerabilities that impede the advancement of cloud services. This study proposes a deterministic storage and deletion mechanism for trusted cloud

The non-linear nature of deep neural networks makes it difficult to interpret the reason behind their output, thus reducing verifiability of the system where these models are applied. Understanding the patterns between activation vectors and predictions could give insight as to erroneous classifications and how to identify them. This paper explains a systematic approach to identifying the clusters

The Internet of Medical Things (IoMT) is transforming healthcare by allowing the storage of patient data for diagnostics and treatment. However, this technology faces significant challenges, including ensuring data reliability, security, quality, and privacy. This study proposes a new architecture that uses Federated Learning (FL) and BigchainDB to address these issues. By using FL and BigchainDB,

As an emerging distributed machine learning paradigm, federated learning has been extensively used in the domain of cloud–edge computing to collaboratively train models without uploading their raw data. However, the existing federated learning methods make an effort to train a single optimal model that encompasses all participating clients. These methods may perform poorly on some clients due to variations

With the rapid development of the Industrial Internet of Things (IIoT), ensuring the security and privacy of network traffic has become particularly important. Classifying and identifying encrypted traffic is a critical step in enhancing network security, but traditional traffic classification methods often struggle to handle the complexities of the IIoT environment. In this paper, we propose the BCBA

Vulnerabilities in unpatched applications can originate from third-party dependencies in statically linked applications, as they must be relinked each time to take advantage of libraries that have been updated to fix any vulnerability. Despite this, malware binaries are often statically linked to ensure they run on target platforms and to complicate malware analysis. In this sense, identification of

Edit distance is a computational grand challenge problem to quantify the minimum number of editing operations required to modify one string of characters to the other, finding many applications of natural language processing. In recent years, relevant and increasing interest has also emerged from deoxyribonucleic acid (DNA) applications, like Next Generation Sequencing and DNA storage technologies

Genomes hold the complete genetic information of an organism. Examining and analyzing genomic data plays a critical role in properly understanding an organism, particularly the main characteristics, functionalities, and evolving nature of harmful viruses. However, the rapid increase in genomic data poses new challenges and demands for extracting meaningful and valuable insights from large and complex

Enterprises are designing and implementing the Internet of Things (IoT), which is envisioned as the next generation of the Internet. Utilizing IoT technologies, different enterprise applications with different enterprise architectures (EAs) have been developed in mining, manufacturing, food location, power, and environmental monitoring systems. Enterprise Architecture plays a critical role in enterprise

Edge applications are increasingly empowered by deep neural networks (DNN) and face the challenges of adapting or retraining models for the changes in input data domains and learning tasks. The existing techniques to enable DNN retraining on edge devices are to configure the memory-related hyperparameters, termed m-hyperparameters, via batch size reduction, parameter freezing, and gradient checkpoint