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

• URL: http://arxiv.org/abs/2507.14181v1
• Date: Sat, 12 Jul 2025 10:54:23 GMT
• Title: Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis
• Authors: Yajiao Dai, Jun Li, Zhen Mei, Yiyang Ni, Shi Jin, Zengxiang Li, Sheng Guo, Wei Xiang,
• Abstract summary: This paper proposes a semi-supervised federated learning framework, SSFL-DCSL.<n>It integrates dual contrastive loss and soft labeling to address data and label scarcity for distributed clients.<n>It can improve accuracy by 1.15% to 7.85% over state-of-the-art methods.
• Score: 30.60728200709919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• (FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning [4.803231218533992]
  Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. Most FL approaches assume that clients possess labeled data, which is often not the case in practice. We propose $(FL)2$, a robust training method for unlabeled clients using sharpness-aware consistency regularization.
  arXiv  Detail & Related papers  (2024-10-30T17:15:02Z)
• FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients [19.3885479917635]
  Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices. We propose FedAnchor, an innovative FSSL method that introduces a unique double-head structure, called anchor head, paired with the classification head trained exclusively on labeled anchor data on the server. Our approach mitigates the confirmation bias and overfitting issues associated with pseudo-labeling techniques based on high-confidence model prediction samples.
  arXiv  Detail & Related papers  (2024-02-15T18:48:21Z)
• Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection [3.7398615061365206]
  Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources. It faces challenges with limited high-quality labels and non-IID client data, particularly in applications like autonomous driving. We present a pioneering SSFOD framework, designed for scenarios where labeled data reside only at the server while clients possess unlabeled data.
  arXiv  Detail & Related papers  (2023-10-26T01:40:28Z)
• VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
  We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data. Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
  arXiv  Detail & Related papers  (2022-05-31T17:45:30Z)
• FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels [49.47228898303909]
  Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Local training on noisy labels can easily result in overfitting to noisy labels, which is devastating to the global model through aggregation. We develop a simple two-level sampling method "FedNoiL" that selects clients for more robust global aggregation on the server.
  arXiv  Detail & Related papers  (2022-05-20T12:06:39Z)
• Uncertainty Minimization for Personalized Federated Semi-Supervised Learning [15.123493340717303]
  We propose a novel semi-supervised learning paradigm which allows partial-labeled or unlabeled clients to seek labeling assistance from data-related clients (helper agents) Experiments show that our proposed method can obtain superior performance and more stable convergence than other related works with partial labeled data.
  arXiv  Detail & Related papers  (2022-05-05T04:41:27Z)
• Class-Aware Contrastive Semi-Supervised Learning [51.205844705156046]
  We propose a general method named Class-aware Contrastive Semi-Supervised Learning (CCSSL) to improve pseudo-label quality and enhance the model's robustness in the real-world setting. Our proposed CCSSL has significant performance improvements over the state-of-the-art SSL methods on the standard datasets CIFAR100 and STL10.
  arXiv  Detail & Related papers  (2022-03-04T12:18:23Z)
• Towards Fair Federated Learning with Zero-Shot Data Augmentation [123.37082242750866]
  Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data. We propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server).
  arXiv  Detail & Related papers  (2021-04-27T18:23:54Z)
• Self-Tuning for Data-Efficient Deep Learning [75.34320911480008]
  Self-Tuning is a novel approach to enable data-efficient deep learning. It unifies the exploration of labeled and unlabeled data and the transfer of a pre-trained model. It outperforms its SSL and TL counterparts on five tasks by sharp margins.
  arXiv  Detail & Related papers  (2021-02-25T14:56:19Z)
• Auto-weighted Robust Federated Learning with Corrupted Data Sources [7.475348174281237]
  Federated learning provides a communication-efficient and privacy-preserving training process. Standard federated learning techniques that naively minimize an average loss function are vulnerable to data corruptions. We propose Auto-weighted Robust Federated Learning (arfl) to provide robustness against corrupted data sources.
  arXiv  Detail & Related papers  (2021-01-14T21:54:55Z)
• WAFFLe: Weight Anonymized Factorization for Federated Learning [88.44939168851721]
  In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices. We propose Weight Anonymized Factorization for Federated Learning (WAFFLe), an approach that combines the Indian Buffet Process with a shared dictionary of weight factors for neural networks.
  arXiv  Detail & Related papers  (2020-08-13T04:26:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.