On the Robustness of Variational Quantum Classifier Against “Label Flipping Attacks” in Federated Learning for Semiconductor Manufacturing

Due to the widespread presence of manufacturing units, real-time data collection becomes a challenging task, especially as the scale of operations continues to expand. Moreover, manufacturing industries often deal with sensitive and proprietary data and are reluctant to share data directly in areas like lithography hotspot detection (LHD) due to concerns about IP protection and regulatory compliance. Moreover, human error can lead to incorrect labeling of parts or products. Detecting hotspots in lithography is crucial for ensuring the product quality and reliability of the manufacturing process. In today's context, Federated Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine learning while effectively managing critical concerns regarding data privacy and governance. Moreover, FL systems must be robust against not only malicious label flipping attacks but also inadvertent human errors. In recent years, the convergence of quantum computing and machine learning represents a groundbreaking interdisciplinary approach with immense potential for revolutionizing various industries ranging from healthcare to manufacturing. In this work, we proposed a Quantum Federated Learning (QFL) framework for lithography hotspot detection and offer a unique solution to address the challenges mentioned above effectively. To demonstrate the generalization ability and robustness, we performed extensive experiments with label-flipping attacks in the proposed framework. Based on our results, the global quantum model maintained a high classification accuracy and best performance even when faced with label-flipping attacks from a specific number of clients in federated learning settings, while keeping data private across manufacturing units. Moreover, a defense strategy based on clustering is proposed to distinguish the good and bad model updates from the participants in a quantum federated framework.