The challenge of data scarcity hinders the application of deep learning in industrial surface defect classification (SDC), as it's 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 global 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, we develop a dynamic consensus construction strategy to mitigate the performance degradation caused by data heterogeneity. 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, we propose a consensus-aware aggregation mechanism. It assigns aggregation weights to different clients based on their efficacy in global knowledge learning, thereby enhancing the global model's generalization capabilities. Finally, we design an adaptive feature fusion module to further enhance global knowledge utilization efficiency. 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 three SDC datasets.

翻译：数据稀缺性挑战阻碍了深度学习在工业表面缺陷分类中的应用，由于隐私顾虑，难以从工业物联网中的各实体收集并集中充足的训练数据。联邦学习通过使客户端在保持隐私的同时进行协作式全局模型训练，为此提供了解决方案。然而，由于数据异构性——即客户端间数据分布的差异——性能可能受到影响。本文针对表面缺陷分类中不同客户端间的数据异构性挑战，提出了一种新颖的个性化联邦学习方法，命名为对抗性联邦共识学习。首先，我们开发了一种动态共识构建策略，以减轻数据异构性导致的性能下降。通过对抗训练，来自不同客户端的本地模型以全局模型为桥梁实现分布对齐，缓解全局知识遗忘问题。作为该策略的补充，我们提出了一种共识感知聚合机制。该机制根据各客户端在全局知识学习中的有效性为其分配聚合权重，从而增强全局模型的泛化能力。最后，我们设计了一个自适应特征融合模块，以进一步提升全局知识利用效率。为每个客户端逐步调整个性化融合权重，以最优方式平衡全局与局部特征。与FedALA等先进联邦学习方法相比，所提出的AFedCL方法在三个表面缺陷分类数据集上实现了高达5.67%的准确率提升。