most citedFederated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting

1 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI2025

Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets

Nikolaos Pavlidis, Vasilis Perifanis, Symeon Symeonidis +1

Large Language Models (LLMs), originally developed for natural language processing (NLP), have demonstrated the potential to generalize across modalities and domains. With their in…

cs.CR2025

Evaluating the Defense Potential of Machine Unlearning against Membership Inference Attacks

Theodoros Tsiolakis, Vasilis Perifanis, Nikolaos Pavlidis +3

Membership Inference Attacks (MIAs) pose a significant privacy risk by enabling adversaries to determine if a specific data point was part of a model's training set. This work empi…

cs.LG20241 cited

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…

cs.LG2024

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…

cs.HC20241 cited

INDIANA: Personalized Travel Recommendations Using Wearables and AI

Anastasios Manos, Despina Elisabeth Filipidou, Ioannis Deliyannis +3

This work presents a personalized travel recommendation system developed as part of the INDIANA platform, designed to enhance the tourist experience through tailored activity sugge…

cs.CY2024

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…