Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning
In this study, we aim to bridge the gap between the theoretical understanding of attacks against collaborative machine learning workflows and their practical ramifications by considering the effects of model architecture, learning setting and hyperparameters on the resilience against attacks. We ref...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Sciendo
2022
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/56cd977fda7b4e01ba8ccebbda7d6e6e |
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