all AI news
Secure Aggregation for Federated Learning in Flower. (arXiv:2205.06117v1 [cs.LG])
May 13, 2022, 1:11 a.m. | Kwing Hei Li, Pedro Porto Buarque de Gusmão, Daniel J. Beutel, Nicholas D. Lane
cs.LG updates on arXiv.org arxiv.org
Federated Learning (FL) allows parties to learn a shared prediction model by
delegating the training computation to clients and aggregating all the
separately trained models on the server. To prevent private information being
inferred from local models, Secure Aggregation (SA) protocols are used to
ensure that the server is unable to inspect individual trained models as it
aggregates them. However, current implementations of SA in FL frameworks have
limitations, including vulnerability to client dropouts or configuration
difficulties.
In this paper, …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
DevOps Engineer (Data Team)
@ Reward Gateway | Sofia/Plovdiv