Oct 30, 2023 · We propose a novel framework called representation-encoding-based federated meta-learning (REFML) for few-shot FD.
Specifically, a novel training strategy based on representation encoding and meta-learning has been invented, which harnesses data heterogeneity among training.
Nov 16, 2024 · To tackle these issues, we propose a novel framework called representation encoding-based federated meta-learning (REFML) for few-shot FD. First ...
A novel FL framework called federated meta-learning based on fine-grained classifier reconstruction (FedFGCR) is presented in this paper.
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This paper introduces a pioneering federated transfer fault diagnosis method that integrates Variational Auto-Encoding (VAE) for robust feature extraction with ...
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What is the role of meta-learning for few shot learning?
What is federated meta-learning?
Chen et al. [151] introduced FedMeta-FFD, a pioneering method that combines federated learning and metalearning to address the few-shot fault diagnosis ...
To address the data scarcity challenge (i.e., few-shot), we propose a collaborative learning method that incorporates meta-learning into the federated learning.
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This study proposes a meta-learning approach utilizing an elastic prototypical network (EProtoNet) for few-shot fault transfer diagnosis in scenarios ...
Sep 19, 2023 · This article introduces a novel federated few-shot fault-diagnosis method called FedCDAE-MN. FedCDAE-MN employs a convolutional denoising auto-encoder and ...
[26] employed a federated meta-learning approach combining representation encoding with MAML to enhance performance in distributed fault diagnosis tasks.