Wednesday, June 19, 2024

Artificial Intelligence news

I tested out a...

This story first appeared in China Report, MIT Technology Review’s newsletter about...

Meta has created a...

Meta has created a system that can embed hidden signals, known as...

Why artists are becoming...

This story originally appeared in The Algorithm, our weekly newsletter on AI....

Why does AI hallucinate?

MIT Technology Review Explains: Let our writers untangle the complex, messy world...
HomeMachine LearningDifferentially Private Heavy...

Differentially Private Heavy Hitter Detection using Federated Analytics



We study practical heuristics to improve the performance of prefix-tree based algorithms for differentially private heavy hitter detection. Our model assumes each user has multiple data points and the goal is to learn as many of the most frequent data points as possible across all users’ data with aggregate and local differential privacy. We propose an adaptive hyperparameter tuning algorithm that improves the performance of the algorithm while satisfying computational, communication and aggregate privacy constraints. We explore the impact of different data-selection schemes as well as the…



Article Source link and Credit

Continue reading

Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials

In practice, training using federated learning can be orders of magnitude slower than standard centralized training. This severely limits the amount of experimentation and tuning that can be done, making it challenging to obtain good performance on a...

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024 Article Source link and Credit

Introducing Apple’s On-Device and Server Foundation Models

Introducing Apple’s On-Device and Server Foundation Models Article Source link and Credit