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cs.CR2026
Potentials and Pitfalls of Applying Federated Learning in Hardware Assurance
Gijung Lee, Wavid Bowman, Olivia Dizon-Paradis +4
As microelectronics flourish and outsourcing of the design and manufacturing stages of integrated circuits (ICs) and printed circuit boards (PCBs) becomes the norm, microelectronic…
cs.CR2026
DECIFR: Domain-Aware Exfiltration of Circuit Information from Federated Gradient Reconstruction
Gijung Lee, Wavid Bowman, Olivia P. Dizon-Paradis +4
Federated Learning (FL) is a promising approach for multiparty collaboration as a privacy-preserving technique in hardware assurance, but its security against adversaries with doma…
cs.CR2026
A Data-Free Membership Inference Attack on Federated Learning in Hardware Assurance
Gijung Lee, Wavid Bowman, Olivia P. Dizon-Paradis +4
Federated Learning (FL) is an emerging solution to the data scarcity problem for training deep learning models in hardware assurance. While FL is designed to enhance privacy by not…