Cerca risorse accademiche

Microscopic traffic flow models and simulations are crucial for capturing vehicle interactions and analyzing traffic. They can provide critical insights for transport planning, management, and operation through scenario ...

Artificial Intelligence (AI) has become a key enabler of intelligent vehicular (V2X) and Unmanned Aerial Vehicle (UAV) networks. These networks face extreme mobility and rapidly changing topologies, making stable communi...

Abstract Federated Learning (FL) has been widely used in Internet of Things (IoT) environments as a promising decentralized framework capable of collaborative model training without exposing local data. Despite its advan...

Federated learning (FL) in mobile edge computing (MEC) operates over wireless links with time-varying bandwidth, latency, and packet loss, which makes conventional full-model synchronization inefficient and vulnerable to...

Federated Learning (FL) is widely recognized as a privacy-preserving architecture, yet the ontological status of the "global model" within this distributed system remains under-theorized. This study challenges the purely...

Federated learning (FL) is a decentralized machine learning (ML) approach that can be used for intrusion detection in Internet of Things (IoT) devices. It involves the local training of AI models and their aggregation at...

Cooperative driving automation (CDA) has gained attention over the years because of its cooperative driving capability that provides solution to individual automated driving challenges. Although reliance on communication...

Abstract Object classification in decentralized environments is critically hindered by data heterogeneity and privacy constraints, which cause models to learn spurious, client-specific correlations rather than generaliza...

Abstract The increasing adoption of Industrial Internet of Things (IIoT) devices introduces significant cybersecurity and privacy challenges, particularly anomaly detection and secure data sharing. This study presents Se...

Industry 4.0 has brought numerous advantages, such as increasing productivity through automation. However, it also presents major cybersecurity issues, such as cyberattacks affecting industrial processes. Federated Learn...

Abstract Precision oncology relies on access to high-quality data for increasingly smaller patient subgroups. The international atomCAT consortium investigates the potential of federated learning to support this, using a...

Federated learning (FL) is a pivotal paradigm for financial institutions to collaboratively train models while preserving data privacy, yet standard frameworks are hampered by a triad of challenges: vulnerability to pois...

Federated Learning (FL) has emerged as a privacy-preserving distributed learning paradigm that enables collaborative model training without sharing raw data. Despite its advantages, ensuring trustworthy FL deployment rem...

Infant cry paralinguistics provides a non-invasive signal for early assessment of physiological and affective states. Yet, most learning approaches implicitly assume that the mapping between acoustic patterns and semanti...

Abstract With a growing number of institutions involved in the global education market, it has become increasingly challenging to verify the authenticity of academic documents and to match them between universities. The ...

Travel behavior analysis provides critical insights to enhance the intelligence of transportation systems, enabling more accurate and efficient management of mobility services. However, it requires centralizing user-sens...

Background: The development of accurate artificial intelligence (AI) models for liver cancer diagnosis using contrast-enhanced computed tomography (CT) is often hindered by patient privacy regulations and considerable da...

With the constant advancement of the smart grid, zero-day attack processes in Advanced Metering Infrastructure (AMI) and Supervisory Control and Data Acquisition (SCADA) networks are also continually evolving. This study...

Abstract The explosion of patient data, the demand for real-time insights, and the critical importance of data security drive healthcare innovation. Medical Internet of Things (IoMT) offers a promising solution, connecti...

Unmanned aerial vehicles (UAVs) are increasingly crucial across various fields. There is a growing interest in using federated learning (FL) methods to enhance the efficiency of UAV operations. Nevertheless, incumbent me...

Federated Prompt Learning (FPL) enables efficient collaborative adaptation of large-scale models, but it remains vulnerable to data heterogeneity: locally optimized prompts may become biased, thereby weakening the aggreg...

The rapid evolution of sustainability legislation, including the CSDDD and CSRD, has created a complex dual mandate for modern enterprises. Organizations are now required to provide granular transparency into their suppl...

The power system event detection process must accelerate and become more precise with increasing penetration of renewable energy systems into the grid. The authors discuss a federated learning LSTM (FL-LSTM) that serves ...

Abstract Cloud computing is quite popular because of its scalability and flexibility merits. However, faces significant security risks from malware, which exploits infrastructure weaknesses to cause unauthorized access, ...