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Found 4 papers, 2 papers with code
Differentially Private Training of Mixture of Experts Models
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.
Packing Privacy Budget Efficiently
This DP budget can be regarded as a new type of compute resource in workloads of multiple ML models training on user data.
Privacy Budget Scheduling
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.
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
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.
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