6 papers
Contrastive Self-Supervised Learning at the Edge: An Energy Perspective
Fernanda Famá, Roberto Pereira, Charalampos Kalalas +4
While contrastive learning (CL) shows considerable promise in self-supervised representation learning, its deployment on resource-constrained devices remains largely underexplored.…
Self-Supervised Learning at the Edge: The Cost of Labeling
Roberto Pereira, Fernanda Famá, Asal Rangrazi +3
Contrastive learning (CL) has recently emerged as an alternative to traditional supervised machine learning solutions by enabling rich representations from unstructured and unlabel…
AI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration
Charalampos Kalalas, Pavol Mulinka, Guillermo Candela Belmonte +10
Artificial intelligence (AI) has been increasingly applied to the condition monitoring of vehicular equipment, aiming to enhance maintenance strategies, reduce costs, and improve s…
Energy-Efficient Federated Learning for AIoT using Clustering Methods
Roberto Pereira, Fernanda Famá, Charalampos Kalalas +1
While substantial research has been devoted to optimizing model performance, convergence rates, and communication efficiency, the energy implications of federated learning (FL) wit…
Knowledge Transfer for Collaborative Misbehavior Detection in Untrusted Vehicular Environments
Roshan Sedar, Charalampos Kalalas, Paolo Dini +3
Vehicular mobility underscores the need for collaborative misbehavior detection at the vehicular edge. However, locally trained misbehavior detection models are susceptible to adve…
Measuring Data Similarity for Efficient Federated Learning: A Feasibility Study
Fernanda Famá, Charalampos Kalalas, Sandra Lagen +1
In multiple federated learning schemes, a random subset of clients sends in each round their model updates to the server for aggregation. Although this client selection strategy ai…