3 papers
cs.LG2026
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…
cs.CR2026
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.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…