7 papers
Federated Learning for Early Prediction of EV Charging Demand
Vasilis Perifanis, Foteini Nikolaidou, Nikolaos Pavlidis +2
Accurate forecasting of electric vehicle (EV) charging demand is critical for grid stability, infrastructure planning, and real-time charging optimization. In this work, we study t…
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…
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…
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 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…
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…