Robustness of Quantum Federated Learning (QFL) Against “Label Flipping Attacks” for Lithography Hotspot Detection in Semiconductor Manufacturing
Amandeep Singh Bhatia, Sabre Kais, Muhammad Ashraful Alam
The geographical dispersion of manufacturing units introduces challenges related to latency, network connectivity, data synchronization, communication overhead, scalability, data security, and resource allocation. Manufacturing data often includes sensitive information related to proprietary processes, designs, and technologies. Companies are hesitant to share this data such as lithography hotspot data (LHD) directly due to concerns about privacy and the risk of intellectual property theft. Federated learning provides a solution to address these challenges by allowing companies to collaboratively train models without sharing raw data. It allows companies to adhere to privacy laws while benefiting from shared insights. In recent years, the convergence of quantum machine learning has demonstrated transformative potential by leveraging quantum principles to address complex problems across various industries. In this work, a concept of quantum federated learning consisting of variational quantum circuit is proposed to address the lithography hotspot sharing challenges by providing a privacy-preserving, centralized, and quantum-enhanced framework. It combines the collaborative advantages of federated learning with the unique capabilities of quantum computing, offering a solution that is secure, scalable, and adaptable to the complexities of modern manufacturing environments. The performance evaluations involve scalability and robustness, particularly in the context of label-flipping attacks, which is crucial for validating, optimizing, and enhancing the quantum federated learning framework's applicability to real-world challenges in the manufacturing sector.
IEEE International Reliability Physics Symposium proceedings•Apr 14, 2024•6 citations