Web: http://arxiv.org/abs/2211.10844

Nov. 22, 2022, 2:20 a.m. | Zheng Xu, Maxwell Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan

cs.CR updates on arXiv.org arxiv.org

Small on-device models have been successfully trained with user-level
differential privacy (DP) for next word prediction and image classification
tasks in the past. However, existing methods can fail when directly applied to
learn embedding models using supervised training data with a large class space.
To achieve user-level DP for large image-to-embedding feature extractors, we
propose DP-FedEmb, a variant of federated learning algorithms with per-user
sensitivity control and noise addition, to train from user-partitioned data
centralized in the datacenter. DP-FedEmb combines …

differential privacy privacy

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