Главная
Study mode:
on
1
Introduction
2
Challenges in Federated Learning
3
Simple example
4
Local Differential Privacy
5
What do we need
6
Privacy Mechanism
7
Full Algorithm
8
Metric Differential Privacy
9
Interpolated MBU
10
Conclusion
11
Questions
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore privacy-aware compression techniques for federated learning in this Google TechTalk presented by Kamalika Chaudhuri at the 2022 Workshop on Federated Learning and Analytics. Delve into the challenges of federated learning, local differential privacy, and privacy mechanisms through a simple example. Examine the full algorithm, metric differential privacy, and interpolated MBU. Gain insights from Chaudhuri, a professor at UC San Diego and research scientist at Meta AI, as she shares her expertise in computer science and engineering. Conclude with a Q&A session to further understand the implications of privacy-aware compression in federated learning environments.

Privacy-Aware Compression for Federated Learning

Google TechTalks
Add to list