Using federated machine learning to overcome the AI scale disadvantage a promising new approach to training AI models lets companies with small data sets collaborate while safeguarding proprietary information
Big Tech companies such as Google, Microsoft, and Amazon have an AI advantage thanks to the vast amounts of data that they collect through their respective platforms. Using federated machine learning (FedML) technology, companies with access to relatively small data sets can join forces in collabora...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
[Cambridge, Massachusetts] :
MIT Sloan Management Review
2023.
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| Edición: | [First edition] |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009820341606719 |
| Sumario: | Big Tech companies such as Google, Microsoft, and Amazon have an AI advantage thanks to the vast amounts of data that they collect through their respective platforms. Using federated machine learning (FedML) technology, companies with access to relatively small data sets can join forces in collaborative artificial intelligence projects while keeping proprietary data private. FedML could be a game changer in bridging the digital divide between organizations with and without big data. |
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| Notas: | "Reprint #65111." |
| Descripción Física: | 1 online resource (7 pages) : illustrations |
