A polynomial proxy model approach to verifiable decentralized federated learning
Abstract Decentralized Federated Learning improves data privacy and eliminates single points of failure by removing reliance on centralized storage and model aggregation in distributed computing systems. Ensuring the integrity of computations during local model training is a significant challenge, e...
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| Autori principali: | , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Nature Portfolio
2024-11-01
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| Serie: | Scientific Reports |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1038/s41598-024-79798-x |
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