|
|
|
|
|
Mikael Sabuhi, Petr Musilek and Cor-Paul Bezemer
As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges a...
ver más
|
|
|
|
|
|
|
|
|
|
|
Changhao Wu, Siyang He, Zengshan Yin and Chongbin Guo
Large-scale low Earth orbit (LEO) remote satellite constellations have become a brand new, massive source of space data. Federated learning (FL) is considered a promising distributed machine learning technology that can communicate optimally using these ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ishaani Priyadarshini
The swift proliferation of the Internet of Things (IoT) devices in smart city infrastructures has created an urgent demand for robust cybersecurity measures. These devices are susceptible to various cyberattacks that can jeopardize the security and funct...
ver más
|
|
|
|
|
|
|
|
|
|
|
Aristeidis Karras, Anastasios Giannaros, Christos Karras, Leonidas Theodorakopoulos, Constantinos S. Mammassis, George A. Krimpas and Spyros Sioutas
In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces ...
ver más
|
|
|
|
|
|
|
|
|
|
|
David Naseh, Mahdi Abdollahpour and Daniele Tarchi
This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed in...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yankai Lv, Haiyan Ding, Hao Wu, Yiji Zhao and Lei Zhang
Federated learning (FL) is an emerging decentralized machine learning framework enabling private global model training by collaboratively leveraging local client data without transferring it centrally. Unlike traditional distributed optimization, FL trai...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ali Abbasi Tadi, Saroj Dayal, Dima Alhadidi and Noman Mohammed
The vulnerability of machine learning models to membership inference attacks, which aim to determine whether a specific record belongs to the training dataset, is explored in this paper. Federated learning allows multiple parties to independently train a...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shobhit Aggarwal and Asis Nasipuri
The Internet of Things (IoT) enables us to gain access to a wide range of data from the physical world that can be analyzed for deriving critical state information. In this regard, machine learning (ML) is a valuable tool that can be used to develop mode...
ver más
|
|
|
|
|
|
|
|
|
|
|
Fotis Nikolaidis, Moysis Symeonides and Demetris Trihinas
Federated learning (FL) is a transformative approach to Machine Learning that enables the training of a shared model without transferring private data to a central location. This decentralized training paradigm has found particular applicability in edge ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jadil Alsamiri and Khalid Alsubhi
In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort....
ver más
|
|
|
|
|
|