71 citations · 76 across the 11 of their papers we have counts for
13 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…
SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing
Vasilis Perifanis, Nikolaos Pavlidis, Symeon Symeonidis
Large language model (LLM) routing aims to select the most suitable model for each incoming query. Most existing routers learn this decision directly from query embeddings, model r…
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…
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…
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…
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…
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…
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…
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,…