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cs.LG2025★ 1 cited
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks
Yohannis Kifle Telila, Damitha Senevirathne, Dumindu Tissera +3
Anomaly detection is crucial in the energy sector to identify irregular patterns indicating equipment failures, energy theft, or other issues. Machine learning techniques for anoma…
cs.LG2024★ 7 cited
Generating Universal Adversarial Perturbations for Quantum Classifiers
Gautham Anil, Vishnu Vinod, Apurva Narayan
Quantum Machine Learning (QML) has emerged as a promising field of research, aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodolo…