3 papers
cs.LG2025
Merge Now, Regret Later: The Hidden Cost of Model Merging Is Adversarial Transferability
Mauro Conti, Ankit Gangwal, Aaryan Ajay Sharma
Model Merging (MM) has proven to be an effective alternative to multi-task learning, where several fine-tuned models are merged, without access to the tasks' training data, into on…
cs.CR2025
KeTS: Kernel-based Trust Segmentation against Model Poisoning Attacks
Ankit Gangwal, Mauro Conti, Tommaso Pauselli
Federated Learning (FL) enables multiple users to collaboratively train a global model in a distributed manner without revealing their personal data. However, FL remains vulnerable…
cs.CR2023
De-authentication using Ambient Light Sensor
Ankit Gangwal, Aashish Paliwal, Mauro Conti
While user authentication happens before initiating or resuming a login session, de-authentication detects the absence of a previously-authenticated user to revoke her currently ac…