Sample selection using multi-task autoencoders in federated learning with non-IID data
Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. However, its performance is often hindered by redundant, malicious, or abnormal samples, leading to model degradation and i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098624003069 |