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Personalized Federated Learning Algorithm Based on Composite Feature Extractor
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AI
DOI：10.1109/JIOT.2026.3667304.png)
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Abstract
En 中文
In the Internet of Things (IoT) environment, to maintain the global consistency and generalization capability of federated learning (FL) models with good data privacy, a personalized FL algorithm is proposed based on a composite feature extractor (CFE), i.e., FedCFE. The FedCFE framework consists of three core modules: a CFE, a conditional generator (CG), and a knowledge distillation mechanism. By modularly combining global with local feature extractors, the proposed CFE enables device-specific personalization without disrupting global knowledge sharing. Meanwhile, the representative pseudo-data is produced by a CG, which facilitates effective knowledge distillation from client models to the server model without requiring direct access to private data. Extensive experimental results demonstrate that the FedCFE consistently achieves competitive or superior performance across various heterogeneous data scenarios. On the highly non-IID CIFAR-100 dataset, the FedCFE improves classification accuracy by 2.19% compared with the state-of-the-art algorithms. These results indicate that the FedCFE provides an effective, lightweight, and privacy-preserving solution to realizing collaborative intelligence in resource-constrained and communication-unstable IoT environments.
Keywords:
Conditional generator (CG)
heterogeneous Internet of Things (IoT) data
information security
knowledge distillation
personalized federated learning (PFL)
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