1 code implementation • 1 Oct 2023 • Nikolaos Pavlidis, Vasileios Perifanis, Theodoros Panagiotis Chatzinikolaou, Georgios Ch. Sirakoulis, Pavlos S. Efraimidis
Federated Learning (FL) has emerged as a promising solution for privacy-enhancement and latency minimization in various real-world applications, such as transportation, communications, and healthcare.
1 code implementation • 19 Sep 2023 • Vasileios Perifanis, Nikolaos Pavlidis, Selim F. Yilmaz, Francesc Wilhelmi, Elia Guerra, Marco Miozzo, Pavlos S. Efraimidis, Paolo Dini, Remous-Aris Koutsiamanis
Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation.
no code implementations • 8 Sep 2023 • Christos Chrysanthos Nikolaidis, Vasileios Perifanis, Nikolaos Pavlidis, Pavlos S. Efraimidis
In this paper, we present a federated machine learning (FML) approach that minimizes privacy concerns and enables distributed training, without transferring individual data.
no code implementations • 1 Aug 2023 • Vasileios Perifanis, Ioanna Michailidi, Giorgos Stamatelatos, George Drosatos, Pavlos S. Efraimidis
The presented algorithms have been submitted to the IFMBE Scientific Challenge 2022, part of IUPESM WC 2022.
1 code implementation • 28 Nov 2022 • Vasileios Perifanis, Nikolaos Pavlidis, Remous-Aris Koutsiamanis, Pavlos S. Efraimidis
In this work, we investigate the efficacy of federated learning applied to raw base station LTE data for time-series forecasting.
no code implementations • 21 Dec 2021 • Vasileios Perifanis, George Drosatos, Giorgos Stamatelatos, Pavlos S. Efraimidis
In this work, we present FedPOIRec, a privacy preserving federated learning approach enhanced with features from users' social circles for top-$N$ POI recommendations.
no code implementations • 2 Jun 2021 • Vasileios Perifanis, Pavlos S. Efraimidis
The system, named FedNCF, enables learning without requiring users to disclose or transmit their raw data.