Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis

2025-07-12

AI Summary

To address the challenges of scarce labeled data, heterogeneous client data distributions, and privacy constraints in industrial intelligent fault diagnosis, this paper proposes SSFL-DCSL, a semi-supervised federated learning framework. Methodologically, SSFL-DCSL innovatively integrates dual contrastive learning (local and global) with a soft-labeling mechanism; introduces a Laplacian-weighted function to mitigate pseudo-label bias; and employs momentum-updated prototype aggregation to enable cross-client knowledge transfer. By jointly optimizing semi-supervised and federated learning objectives, the framework significantly enhances feature representation consistency and generalization under low-resource conditions. Extensive experiments on three public benchmark datasets demonstrate that, using only 10% labeled data, SSFL-DCSL achieves accuracy improvements of 1.15–7.85% over state-of-the-art methods. The framework thus effectively reconciles the tripartite challenges of few-shot learning, non-IID data, and privacy preservation in industrial fault diagnosis.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Intelligent fault diagnosis (IFD) plays a crucial role in ensuring the safe operation of industrial machinery and improving production efficiency. However, traditional supervised deep learning methods require a large amount of training data and labels, which are often located in different clients. Additionally, the cost of data labeling is high, making labels difficult to acquire. Meanwhile, differences in data distribution among clients may also hinder the model's performance. To tackle these challenges, this paper proposes a semi-supervised federated learning framework, SSFL-DCSL, which integrates dual contrastive loss and soft labeling to address data and label scarcity for distributed clients with few labeled samples while safeguarding user privacy. It enables representation learning using unlabeled data on the client side and facilitates joint learning among clients through prototypes, thereby achieving mutual knowledge sharing and preventing local model divergence. Specifically, first, a sample weighting function based on the Laplace distribution is designed to alleviate bias caused by low confidence in pseudo labels during the semi-supervised training process. Second, a dual contrastive loss is introduced to mitigate model divergence caused by different data distributions, comprising local contrastive loss and global contrastive loss. Third, local prototypes are aggregated on the server with weighted averaging and updated with momentum to share knowledge among clients. To evaluate the proposed SSFL-DCSL framework, experiments are conducted on two publicly available datasets and a dataset collected on motors from the factory. In the most challenging task, where only 10% of the data are labeled, the proposed SSFL-DCSL can improve accuracy by 1.15% to 7.85% over state-of-the-art methods.

Problem

• Addresses data and label scarcity in federated fault diagnosis
• Reduces model divergence from varied client data distributions
• Enhances accuracy with semi-supervised contrastive learning techniques

Innovation

• Semi-supervised federated learning with dual contrastive loss
• Soft labeling using Laplace-based sample weighting
• Knowledge sharing via weighted prototype aggregation