3 papers
cs.CR2025
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Sayyed Farid Ahamed, Sandip Roy, Soumya Banerjee +6
Federated Learning (FL) is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property (IP) threats. Model extract…
cs.CR2025
RADEP: A Resilient Adaptive Defense Framework Against Model Extraction Attacks
Amit Chakraborty, Sayyed Farid Ahamed, Sandip Roy +6
Machine Learning as a Service (MLaaS) enables users to leverage powerful machine learning models through cloud-based APIs, offering scalability and ease of deployment. However, the…
cs.LG2024
Privacy Drift: Evolving Privacy Concerns in Incremental Learning
Sayyed Farid Ahamed, Soumya Banerjee, Sandip Roy +6
In the evolving landscape of machine learning (ML), Federated Learning (FL) presents a paradigm shift towards decentralized model training while preserving user data privacy. This…