203 citations · 284 across the 10 of their papers we have counts for
11 papers
Federated Art Chase: Privacy Leakage and Data Poisoning in Federated Fine-Tuning
Vasileios Perifanis, Nikolaos Pavlidis
Federated learning (FL) is increasingly used to adapt a pretrained, centrally fine-tuned model to data that remain on user devices, beyond training a model from scratch. In this re…
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting
Nikolaos Pavlidis, Vasileios Perifanis, Selim F. Yilmaz +6
The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular t…
Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction
Theodoros Tsiolakis, Nikolaos Pavlidis, Vasileios Perifanis +1
Cellular traffic forecasting is a critical task that enables network operators to efficiently allocate resources and address anomalies in rapidly evolving environments. The exponen…
Federated Anomaly Detection for Early-Stage Diagnosis of Autism Spectrum Disorders using Serious Game Data
Nikolaos Pavlidis, Vasileios Perifanis, Eleni Briola +4
Early identification of Autism Spectrum Disorder (ASD) is considered critical for effective intervention to mitigate emotional, financial and societal burdens. Although ASD belongs…
Intelligent Client Selection for Federated Learning using Cellular Automata
Nikolaos Pavlidis, Vasileios Perifanis, Theodoros Panagiotis Chatzinikolaou +2
Federated Learning (FL) has emerged as a promising solution for privacy-enhancement and latency minimization in various real-world applications, such as transportation, communicati…
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks
Vasileios Perifanis, Nikolaos Pavlidis, Selim F. Yilmaz +6
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…
Federated Learning for Early Dropout Prediction on Healthy Ageing Applications
Christos Chrysanthos Nikolaidis, Vasileios Perifanis, Nikolaos Pavlidis +1
The provision of social care applications is crucial for elderly people to improve their quality of life and enables operators to provide early interventions. Accurate predictions…
Predicting Early Dropouts of an Active and Healthy Ageing App
Vasileios Perifanis, Ioanna Michailidi, Giorgos Stamatelatos +2
In this work, we present a machine learning approach for predicting early dropouts of an active and healthy ageing app. The presented algorithms have been submitted to the IFMBE Sc…
Federated Learning for 5G Base Station Traffic Forecasting
Vasileios Perifanis, Nikolaos Pavlidis, Remous-Aris Koutsiamanis +1
Cellular traffic prediction is of great importance on the path of enabling 5G mobile networks to perform intelligent and efficient infrastructure planning and management. However,…
FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence
Vasileios Perifanis, George Drosatos, Giorgos Stamatelatos +1
With the growing number of Location-Based Social Networks, privacy preserving location prediction has become a primary task for helping users discover new points-of-interest (POIs)…
Federated Neural Collaborative Filtering
Vasileios Perifanis, Pavlos S. Efraimidis
In this work, we present a federated version of the state-of-the-art Neural Collaborative Filtering (NCF) approach for item recommendations. The system, named FedNCF, enables learn…