FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection

• URL: http://arxiv.org/abs/2607.08014v1
• Date: Thu, 09 Jul 2026 00:40:33 GMT
• Title: FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection
• Abstract summary: Federated learning (FL) is a collaborative learning scheme to train deep learning models.<n>This paper introduces FedTR, a novel FL framework incorporating transfer learning designed for Autonomous IVI.<n>It focuses on the challenging task of identifying label defects through end-to-end text recognition.
• Score: 13.21063374380496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models without sharing local data with other parties, hence preserving data privacy. Nevertheless, when implementing FL in Industrial visual inspection (IVI), the constraints posed by limited data availability and the intricate nature of the inspection tasks significantly impact the performance of the resulting model. This paper introduces FedTR, a novel FL framework incorporating transfer learning designed for Autonomous IVI, focusing on the challenging task of identifying label defects through end-to-end text recognition. Transfer learning is a method that leverages the knowledge of a pre-trained model to adapt to a different dataset. FedTR initially trains the model using a publicly available dataset, after which performs the essential federated learning process with model fine-tuning on the distributed and limited private data. Extensive experiment results demonstrate the effectiveness and feasibility of FedTR on private ink cartridge datasets for label defect identification. FedTR achieves an end-to-end text recognition word-level accuracy of 95.5% and 94.2% on homogeneous and heterogeneous data respectively. Additionally, it attains performance levels that are on par with those achieved through centralized training.

Related papers

• Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning [31.54643729002375]
  Federated unlearning enables the removal of specific data influences from trained models.<n>Federated Optimization for data Removal via primal-dual Algorithm proposed.<n>New unlearning loss function promotes classification uncertainty rather than misclassification.
  arXiv  Detail & Related papers  (2025-12-29T03:25:52Z)
• Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis [30.60728200709919]
  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.
  arXiv  Detail & Related papers  (2025-07-12T10:54:23Z)
• CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning [22.38403602956309]
  We propose CHASe (Client Heterogeneity-Aware Data Selection), specifically designed for Federated Active Learning (FAL)<n> CHASe focuses on identifying those unlabeled samples with high epistemic variations (EVs), which notably oscillate around the decision boundaries during training.<n> Experiments show that CHASe surpasses various established baselines in terms of effectiveness and efficiency, validated across diverse datasets, model complexities, and heterogeneous federation settings.
  arXiv  Detail & Related papers  (2025-04-24T11:28:00Z)
• HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast [10.652998357266934]
  We propose a system heterogeneous federation method based on data-free knowledge distillation and two-way contrast (HFedCKD)<n>HFedCKD effectively alleviates the knowledge offset caused by a low participation rate under data-free knowledge distillation and improves the performance and stability of the model.<n>We conduct extensive experiments on image and IoT datasets to comprehensively evaluate and verify the generalization and robustness of the proposed HFedCKD framework.
  arXiv  Detail & Related papers  (2025-03-09T08:32:57Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Fake It Till Make It: Federated Learning with Consensus-Oriented Generation [52.82176415223988]
  We propose federated learning with consensus-oriented generation (FedCOG) FedCOG consists of two key components at the client side: complementary data generation and knowledge-distillation-based model training. Experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-10T18:49:59Z)
• UNIDEAL: Curriculum Knowledge Distillation Federated Learning [17.817181326740698]
  Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients. In this paper, we present UNI, a novel FL algorithm specifically designed to tackle the challenges of cross-domain scenarios. Our results demonstrate that UNI achieves superior performance in terms of both model accuracy and communication efficiency.
  arXiv  Detail & Related papers  (2023-09-16T11:30:29Z)
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning [87.08902493524556]
  Federated learning(FL) has recently attracted increasing attention from academia and industry. We propose FedDM to build the global training objective from multiple local surrogate functions. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data.
  arXiv  Detail & Related papers  (2022-07-20T04:55:18Z)
• Toward Understanding the Influence of Individual Clients in Federated Learning [52.07734799278535]
  Federated learning allows clients to jointly train a global model without sending their private data to a central server. We defined a new notion called em-Influence, quantify this influence over parameters, and proposed an effective efficient model to estimate this metric.
  arXiv  Detail & Related papers  (2020-12-20T14:34:36Z)
• A Principled Approach to Data Valuation for Federated Learning [73.19984041333599]
  Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources. The Shapley value (SV) defines a unique payoff scheme that satisfies many desiderata for a data value notion. This paper proposes a variant of the SV amenable to FL, which we call the federated Shapley value.
  arXiv  Detail & Related papers  (2020-09-14T04:37:54Z)

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.