Web: http://arxiv.org/abs/2201.11934

Jan. 31, 2022, 2:11 a.m. | Jieren Deng, Chenghong Wang, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, Caiwen Ding

cs.LG updates on arXiv.org arxiv.org

In this work, we consider the problem of designing secure and efficient
federated learning (FL) frameworks. Existing solutions either involve a trusted
aggregator or require heavyweight cryptographic primitives, which degrades
performance significantly. Moreover, many existing secure FL designs work only
under the restrictive assumption that none of the clients can be dropped out
from the training protocol. To tackle these problems, we propose SEFL, a secure
and efficient FL framework that (1) eliminates the need for the trusted
entities; (2) …

arxiv federated learning framework learning nlp

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