FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes

[2505.07315] FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2505.07315 (cs) This paper has been withdrawn by Zexiao Wang [Submitted on 12 May 2025 ( v1 ), last revised 9 Apr 2026 (this version, v3)] Title: FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes Authors: Zexiao Wang , Yankai Wang , Xiaoqiang Liao , Xinguo Ming , Weiming Shen View a PDF of the paper titled FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes, by Zexiao Wang and 4 other authors No PDF available, click to view other formats Abstract: Due to the scarcity of industrial data, individual equipment users, particularly start-ups, struggle to independently train a comprehensive fault diagnosis model; federated learning enables collaborative training while ensuring data privacy, making it an ideal solution. However, the diversity of working conditions leads to variations in fault modes, resulting in inconsistent label spaces across different clients. In federated diagnostic scenarios, label space inconsistency leads to local models focus on client-specific fault modes and causes local models from different clients to map different failure modes to similar feature representations, which weakens the aggregated global model's generalization. To tackle this issue, this article proposed a federated cross-domain diagnostic framework termed Federated Invariant Features Learning (FedIFL). In intra-client training, prototype contrastive learning mitigates intra-client domain shifts, subsequently, feature generating ensures local models can access distributions of other clients in a privacy-friendly manner. Besides, in cross-client training, a feature disentanglement mechanism is introduced to mitigate cross-client domain shifts, specifically, an instance-level federated instance consistency loss is designed to ensure the instance-level consistency of invariant features between different clients, furthermore, a federated instance personalization loss and an orthogonal loss are constructed to distinguish specific features that from the invariant features. Eventually, the aggregated model achieves promising generalization among global label spaces, enabling accurate fault diagnosis for target clients' Motor Driven Systems (MDSs) with inconsistent label spaces. Experiments on real-world MDSs validate the effectiveness and superiority of FedIFL in federated cross-domain diagnosis with inconsistent fault modes. Comments: The paper is being withdrawn as we found that it did not fully articulate the representation of deep implicit features, which is the core focus of our work. Additionally, the experiments were incomplete and lacked sufficient analysis. We plan to revise the paper, clarify these aspects, and enhance the experimental validation before resubmitting Subjects: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG) Cite as: arXiv:2505.07315 [cs.AI] (or arXiv:2505.07315v3 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2505.07315 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zexiao Wang [ view email ] [v1] Mon, 12 May 2025 08:00:49 UTC (1,717 KB) [v2] Fri, 5 Dec 2025 07:36:18 UTC (1 KB) (withdrawn) [v3] Thu, 9 Apr 2026 08:56:39 UTC (1 KB) (withdrawn) Full-text links: Access Paper: View a PDF of the paper titled FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes, by Zexiao Wang and 4 other authors Withdrawn No license for this version due to withdrawn Current browse context: cs.AI < prev | next > new | recent | 2025-05 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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