no code implementations • 11 Feb 2024 • Pierre Tholoniat, Huseyin A. Inan, Janardhan Kulkarni, Robert Sim
This position paper investigates the integration of Differential Privacy (DP) in the training of Mixture of Experts (MoE) models within the field of natural language processing.
no code implementations • 26 Dec 2022 • Pierre Tholoniat, Kelly Kostopoulou, Mosharaf Chowdhury, Asaf Cidon, Roxana Geambasu, Mathias Lécuyer, Junfeng Yang
This DP budget can be regarded as a new type of compute resource in workloads of multiple ML models training on user data.
1 code implementation • 29 Jun 2021 • Tao Luo, Mingen Pan, Pierre Tholoniat, Asaf Cidon, Roxana Geambasu, Mathias Lécuyer
We describe PrivateKube, an extension to the popular Kubernetes datacenter orchestrator that adds privacy as a new type of resource to be managed alongside other traditional compute resources, such as CPU, GPU, and memory.
2 code implementations • 8 Jun 2020 • Théo Ryffel, Pierre Tholoniat, David Pointcheval, Francis Bach
We evaluate our end-to-end system for private inference between distant servers on standard neural networks such as AlexNet, VGG16 or ResNet18, and for private training on smaller networks like LeNet.