相关论文: Blockchain-based Federated Learning for Device Fai…

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the…

The rapid expansion of heterogeneous Internet of Things (IoT) environments has heightened security risks, as resource-constrained devices remain vulnerable to diverse cyberattacks. Federated Learning (FL) has emerged as a privacy-preserving…

Federated Learning (FL) is a privacy-preserving distributed machine learning scheme, where each participant data remains on the participating devices and only the local model generated utilizing the local computational power is transmitted…

While the convergence of Artificial Intelligence (AI) techniques with improved information technology systems ensured enormous benefits to the Internet of Vehicles (IoVs) systems, it also introduced an increased amount of security and…

The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour. Gathering personal data and performing machine learning tasks on this data in a central location presents a…

Critical role of Internet of Things (IoT) in various domains like smart city, healthcare, supply chain and transportation has made them the target of malicious attacks. Past works in this area focused on centralized Intrusion Detection…

IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to…

Sensor data in IoT (Internet of Things) systems is vulnerable to tampering or falsification when transmitted through untrusted services. This is critical because such data increasingly underpins real-world decisions in domains such as…

Energy shortfall and electricity load shedding are the main problems for developing countries. The main causes are lack of management in the energy sector and the use of non-renewable energy sources. The improved energy management and use…

The exponential growth of android-based mobile IoT systems has significantly increased the susceptibility of devices to cyberattacks, particularly in smart homes, UAVs, and other connected mobile environments. This article presents a…

Federated learning is a new approach to distributed machine learning that offers potential advantages such as reducing communication requirements and distributing the costs of training algorithms. Therefore, it could hold great promise in…

Medical health care centers are envisioned as a promising paradigm to handle the massive volume of data of COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and…

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithms for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to…

The rapid growth of the Internet of Things (IoT) offers new opportunities but also expands the attack surface of distributed, resource-limited devices. Intrusion detection in such environments is difficult due to data heterogeneity from…

The rapid growth of industrial Internet of Things (IIoT) systems has created new challenges for anomaly detection in high-dimensional, multivariate time-series, where privacy, scalability, and communication efficiency are critical.…

The industrial Internet of Things (IIoT) involves the integration of Internet of Things (IoT) technologies into industrial settings. However, given the high sensitivity of the industry to the security of industrial control system networks…

Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications. In many applications, such as smart homes with…

Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that…

Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions…

This paper presents a fully coupled blockchain-assisted federated learning architecture that effectively eliminates single points of failure by decentralizing both the training and aggregation tasks across all participants. Our proposed…