Utility–Privacy Trade-Off in Distributed Machine Learning Systems

In distributed machine learning (DML), though clients’ data are not directly transmitted to the server for model training, attackers can obtain the sensitive information of clients by analyzing the local gradient parameters uploaded by clients. For this case, we use the differential privacy (DP) mec...

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Detalles Bibliográficos
Autores principales: Zeng, Xia, Yang, Chuanchuan, Dai, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498028/
https://www.ncbi.nlm.nih.gov/pubmed/36141185
http://dx.doi.org/10.3390/e24091299

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498028/
https://www.ncbi.nlm.nih.gov/pubmed/36141185
http://dx.doi.org/10.3390/e24091299

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