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1
Introduction
2
Federated Learning
3
Multiparty Computation
4
Open Research
5
Applications
6
Differential Privacy
7
Two Models
8
Four Essential Ingredients
9
The Algorithm
10
Algorithm Parameters
11
Results
12
Tradeoffs
13
Deltas
14
Comparison
Description:
Explore federated heavy hitters discovery with differential privacy in this 44-minute lecture by Peter Kairouz from Google AI. Delve into the intersection of privacy and data analysis, covering key topics such as federated learning, multiparty computation, and open research. Examine two models and four essential ingredients of the algorithm, along with its parameters and results. Analyze tradeoffs, deltas, and comparisons to gain a comprehensive understanding of this cutting-edge approach to privacy-preserving data analysis in federated settings.

Federated Heavy Hitters Discovery with Differential Privacy

Simons Institute
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