Towards addressing aggregation deviation for model training in resource-scarce edge environment
Federated Learning (FL) can train models in an edge environment without sending raw data. However, the performance is still constrained by data heterogeneity. To address the problems of data heterogeneity and resource scarcity in edge devices, we propose Federal Learning via Dynamic Aggregation (Fed...
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| Autori principali: | , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Springer
2024-01-01
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| Serie: | Journal of King Saud University: Computer and Information Sciences |
| Soggetti: | |
| Accesso online: | http://www.sciencedirect.com/science/article/pii/S1319157824000016 |
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