Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT

The challenge of data scarcity hinders the application of deep learning in surface defect classification (SDC), as it is difficult to collect and centralize sufficient training data from various entities in Industrial Internet of Things (IIoT) due to privacy concerns. Federated learning (FL) provides a solution by enabling collaborative model training across clients while maintaining privacy. However, performance may suffer due to data heterogeneity—discrepancies in data distributions among clients. In this paper, we propose a novel personalized FL (PFL) approach, named Adversarial Federated Consensus Learning (AFedCL), for the challenge of data heterogeneity across different clients in SDC. First, a dynamic consensus construction strategy is developed. Through adversarial training, local models from different clients utilize the global model as a bridge to achieve distribution alignment, alleviating the problem of global knowledge forgetting. Complementing this strategy, a consensus-aware aggregation mechanism is proposed. It assigns aggregation weights to different clients based on their efficacy in global knowledge learning, thereby enhancing the global model’s generalization capabilities. Finally, an adaptive feature fusion module is designed to achieve further balance between global and local knowledge for each client. Personalized fusion weights are gradually adjusted for each client to optimally balance global and local features. Compared with state-of-the-art FL methods like FedALA, the proposed AFedCL method achieves an accuracy increase of up to 5.67% on four SDC datasets.