Cyber threats are growing in scale and complexity, outpacing the capabilities of traditional security systems. Machine learning (ML) models offer enhanced detection accuracy but often rely on centralized data, raising privacy concerns. Federated lear...
With the rise of social networks and the introduction of data protection laws, companies are training machine learning models using data generated locally by their users or customers in various types of devices. The data may include sensitive informa...
With the development of the power internet of things, the traditional centralized computing pattern has been difficult to apply to many power business scenarios, including power load forecasting, substation defect detection, and demand-side response....
In real-world scenarios, ECG data are collected from a diverse range of heterogeneous devices, including high-end medical equipment and consumer-grade wearable devices, each with varying computational capabilities and constraints. This heterogeneity...
COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the...
The study presents a comprehensive framework for integrating foundation models (FMs), federated learning (FL), and Artificial Intelligence of Things (AIoT) technologies to enhance aircraft health monitoring systems (AHMSs). The proposed architecture...
This paper focuses on the efficient fine-tuning of large language models within the federated learning framework. To address the performance bottlenecks caused by multi-source heterogeneity and structural inconsistency, a structure-aware federated fi...
Bringing together the Internet of Things (IoT), LLMs, and Federated Learning (FL) offers exciting possibilities, creating a synergy to build smarter, privacy-preserving distributed systems. This review explores the merging of these technologies, part...
Large Language Models (LLMs) in Intelligent Computer-Assisted Language Learning enable highly personalized learning, yet raise significant challenges related to pedagogical grounding, data privacy, and instructional validity. Although Knowledge Graph...
By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sens...
The rapid integration of large-scale AI models into distributed systems, such as the Artificial Intelligence of Things (AIoT), has introduced critical security and privacy challenges. While configurable models enhance resource efficiency, their deplo...
Chemical agents are one of the major threats to soldiers in modern warfare, so it is so important to detect chemical agents rapidly and accurately on battlefields. Raman spectroscopy-based detectors are widely used but have many limitations. The Rama...
Federated learning is a novel distributed learning framework, which enables thousands of participants to collaboratively construct a deep learning model. In order to protect confidentiality of the training data, the shared information between server...
Deep learning-based algorithms have led to tremendous progress over the last years, but they face a bottleneck as their optimal development highly relies on access to large datasets. To mitigate this limitation, cross-silo federated learning has emer...
Edge devices employing federated learning encounter several obstacles, including (1) the non-independent and identically distributed (Non-IID) nature of client data, (2) limitations due to communication bottlenecks, and (3) constraints on computation...
A key feature of federated learning (FL) is that not all clients participate in every communication epoch of each global model update. The rationality for such partial client selection is largely to reduce the communication overhead. However, in many...
Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneou...
Recent progress in machine learning frameworks has made it possible to now perform inference with models using cheap, tiny microcontrollers. Training of machine learning models for these tiny devices, however, is typically done separately on powerful...
Federated learning (FL) is a privacy-preserving technique for training a vast amount of decentralized data and making inferences on mobile devices. As a typical language modeling problem, mobile keyboard prediction aims at suggesting a probable next...
The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing...
With the proliferation of mobile devices and wireless communications, Location-Based Social Networks (LBSNs) have seen tremendous growth. Location recommendation, as an important service in LBSNs, can provide users with locations of interest by analy...
Hierarchical asynchronous federated learning (HAFL) accommodates more real networking and ensures practical communications and efficient aggregations. However, existing HAFL schemes still face challenges in balancing privacy-preserving and robustness...
The rapid growth of IoT devices has increased security attack behaviors, posing a challenge to IoT security. Some Federated-Learning-based detection methods have been widely used to detect malicious attacks in the IoT by analyzing network traffic; be...
Effective building energy prediction is essential for optimizing energy management, but existing models struggle with data scarcity and sensor heterogeneity across different buildings. Conventional approaches, including centralized and transfer learn...
Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in th...
Stock price modeling under privacy constraints presents a unique challenge at the intersection of computational economics and machine learning. Financial institutions and brokerage firms hold valuable yet sensitive data that cannot be centrally aggre...
As social networks continue to expand, smart advertising increasingly depends on machine learning to deliver personalized and effective advertisements. Federated Learning (FL) is a distributed learning paradigm that supports privacy-preserving advert...
The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in...
In this work, we formalize the concept of differential model robustness (DMR), a new property for ensuring model security in federated learning (FL) systems. For most conventional FL frameworks, all clients receive the same global model. If there exi...
In the data-driven healthcare sector, balancing privacy protection and model performance is critical. This paper enhances accuracy and reliability in survival analysis by integrating differential privacy, deep learning, and the Cox proportional hazar...
The exchange of gradients is a widely used method in modelling systems for machine learning (e.g., distributed training, federated learning) in privacy-sensitive domains. Unfortunately, there are still privacy risks in federated learning, as servers...
With the advancement of the large language model (LLM), the demand for data labeling services has increased dramatically. Big models are inseparable from high-quality, specialized scene data, from training to deploying application iterations to landi...
Artificial intelligence (AI) and machine learning (ML) have become integral to various applications, leveraging vast amounts of heterogeneous, globally distributed Internet of Things (IoT) data to identify patterns and build accurate ML models for pr...
Benchmarking embodied carbon in residential building stock accurately would involve a high volume of data sharing and would pose serious privacy and competitive issues among building construction stakeholders. This study introduces a new federated le...
Perceived organizational performance (POP) is an important factor that influences employees’ attitudes and behaviors such as retention and turnover, which in turn improve or impede organizational sustainability. The current study aims to identify int...
The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quali...
With the rapid development of the Industrial Internet of Things (IIoT) and intelligent manufacturing, massive amounts of heterogeneous and non-independent, identically distributed (non-IID) data are continuously generated in industrial environments....
Considering the sensitivity of data in medical scenarios, federated learning (FL) is suitable for applications that require data privacy. Medical personnel can use the FL framework for machine learning to assist in analyzing large-scale data that are...
Background/Objectives: Accurate skeletal classification is essential for orthodontic diagnosis. This study evaluates the effectiveness of federated convolutional neural network (CNN) models for skeletal classification using cephalometric images from...
With the rapid advancement of the Internet and big data technologies, traditional centralized machine learning methods are challenged when dealing with large-scale datasets. Federated Learning (FL), as an emerging distributed machine learning paradig...
The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning tech...
Predictive maintenance (PdM) is vital to maritime operations; however, the traditional deep learning solutions currently offered heavily depend on centralized data aggregation, which is impractical under the limited connectivity, privacy concerns, an...
In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and...
Hierarchical Federated Learning (HFL) alleviates the trade-off between communication overhead and privacy protection in mobile scenarios via multi-level aggregation and mobility consideration. However, its idealized convex loss assumption and full-di...
Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling decentralized model training across multiple devices while preserving data privacy. However, the decentralized nature of FL introduces significant security...
This research article presents a novel road defect detection methodology that integrates deep learning techniques and a federated learning approach. Existing road defect detection systems heavily rely on manual inspection and sensor-based techniques,...
Fall detection is a critical task in healthcare monitoring systems, especially for elderly populations, for whom timely intervention can significantly reduce morbidity and mortality. This study proposes a privacy-preserving and scalable fall-detectio...
Background: Industry 4.0’s development requires digitalized manufacturing through Predictive Maintenance (PdM) because such practices decrease equipment failures and operational disruptions. However, its effectiveness is hindered by three key c...
Federated learning is a privacy-preserving distributed machine learning paradigm. However, due to client data heterogeneity, the global model trained by a traditional federated averaging algorithm often exhibits poor generalization ability. To mitiga...
Federated learning (FL) has been broadly adopted in both academia and industry in recent years. As a bridge to connect the so-called “data islands”, FL has contributed greatly to promoting data utilization. In particular, FL enables disjo...
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