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Found 4 papers, 1 papers with code
Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives
This paper proposes a locally differentially private federated learning algorithm for strongly convex but possibly nonsmooth problems that protects the gradients of each worker against an honest but curious server.
Personalized Privacy Amplification via Importance Sampling
We first consider the general case where an arbitrary personalized differentially private mechanism is subsampled with an arbitrary importance sampling distribution and show that the resulting mechanism also satisfies personalized differential privacy.
Decentralized Differentially Private Segmentation with PATE
For this reason, we turn our attention to Private Aggregation of Teacher Ensembles (PATE), where all local models can be trained independently without inter-institutional communication.
Implementing Adaptive Separable Convolution for Video Frame Interpolation
As Deep Neural Networks are becoming more popular, much of the attention is being devoted to Computer Vision problems that used to be solved with more traditional approaches.
