Class Imbalance Wafer Defect Pattern Recognition Based on Shared-Database Decentralized Federated Learning Framework

Abstract

In this article, a novel shared-database decentralized federated learning (SDeceFL) framework is developed for wafer defect pattern recognition (DPR). Specifically, a differential privacy shared-database strategy is proposed to overcome the interclass heterogeneity problem of different clients and enhance data privacy. A deformable convolutional autoencoder (DCAE) is designed for data augmentation for handling class imbalance. The vision transformer (ViT) is employed for wafer DPR. The proposed DCAE-ViT-SDeceFL framework is validated on three public datasets (e.g., WM-811K, NEU-CLS-64, and CIFAR-100). The experimental results show the superiority of the SDeceFL framework over Ratio Loss-FedAvg, MOON, FedNH, BalanceFL, federated averaging (FedAvg), DeceFL, and swarm learning (SL). Compared with some deep learning methods, experimental results exhibit the effectiveness of the proposed DCAE-ViT-SDeceFL method for wafer DPR on WM-811K.