432 citations
- Centre National de la Recherche ScientifiqueFR5 papers
- Istanbul Technical UniversityTR5 papers
- University of WolverhamptonGB4 papers
- Helmholtz-Zentrum Berlin für Materialien und EnergieDE3 papers
- Observatoire de ParisFR3 papers
- Queen's University BelfastGB3 papers
- Shanghai Ocean UniversityCN3 papers
- University of GlasgowGB3 papers
- Chinese Academy of SciencesCN2 papers
- Fudan UniversityCN2 papers
- Janssen (France)FR2 papers
- Laboratoire Univers et ThéoriesFR2 papers
8 papers · 1 filter
AI in Energy Digital Twining: A Reinforcement Learning-based Adaptive Digital Twin Model for Green Cities
Lal Verda Cakir, Kubra Duran, Craig Thomson +2
Digital Twins (DT) have become crucial to achieve sustainable and effective smart urban solutions. However, current DT modelling techniques cannot support the dynamicity of these s…
Transfer Learning via Test-Time Neural Networks Aggregation
Bruno Casella, Alessio Barbaro Chisari, Sebastiano Battiato +1
It has been demonstrated that deep neural networks outperform traditional machine learning. However, deep networks lack generalisability, that is, they will not perform as good as…
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
Pavlos Papadopoulos, Oliver Thornewill von Essen, Nikolaos Pitropakis +3
As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Interne…
Practical Defences Against Model Inversion Attacks for Split Neural Networks
Tom Titcombe, Adam J. Hall, Pavlos Papadopoulos +1
We describe a threat model under which a split network-based federated learning system is susceptible to a model inversion attack by a malicious computational server. We demonstrat…
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
Daniele Romanini, Adam James Hall, Pavlos Papadopoulos +6
We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on…
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
Indro Spinelli, Simone Scardapane, Amir Hussain +1
Graph representation learning has become a ubiquitous component in many scenarios, ranging from social network analysis to energy forecasting in smart grids. In several application…