Federated learning with heterogeneous data and models based on global decision boundary distillation
Abstract Data heterogeneity and performance disparities among heterogeneous models are critical challenges in federated learning with heterogeneous data and models, which limit its practical applicability and degrade local model performance. To address these challenges, we propose Federated Learning...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00097-0 |
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