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

TL;DR

A novel personalized FL (PFL) approach, named adversarial federated consensus learning (AFedCL), for the challenge of data heterogeneity across different clients in SDC, which achieves an accuracy increase of up to 5.67% on four SDC datasets.

Abstract

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.

Figures (10)

• Figure 1: Illustration of the FL architecture for IIoT scenarios.
• Figure 2: Illustration of non-IID data in IIoT. Each client has a distinct data distribution, which can potentially degrade the global model's performance.
• Figure 3: The workflow of the proposed AFedCL method. It comprises a central server and multiple clients, each with heterogeneous and limited training data. Through multiple communication rounds, the server and clients collaboratively train personalized models that are specifically tailored to each client’s unique local data characteristics, all while maintaining privacy and operating within the constraints of limited data.
• Figure 4: Visualization of the consensus construction process. (a) discrimination loss. (b) discrimination accuracy.
• Figure 5: Relationship between discrimination loss and adaptive fusion weight.