Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning
Federated Learning (FL) has been proposed as a privacy-preserving solution for distributed machine learning, particularly in heterogeneous FL settings where clients have varying computational capabilities and thus train models with different complexities compared to the server’s model. Howeve...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
IEEE
2025-01-01
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| coleção: | IEEE Access |
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| Acesso em linha: | https://ieeexplore.ieee.org/document/10906485/ |
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