Federated Design of Compact and Private DNNs

Federated Design of Compact and Private DNNs

Subscribers:
349,000
Published on ● Video Link: https://www.youtube.com/watch?v=ps5PcNVc3Vw



Duration: 17:52
203 views
3


A Google TechTalk, 2020/7/29, presented by Farinaz Koushanfar, UCSD
ABSTRACT:




Other Videos By Google TechTalks


2021-09-29A Geometric View on Private Gradient-Based Optimization
2021-09-29BB84: Quantum Protected Cryptography
2021-09-29Fast and Memory Efficient Differentially Private-SGD via JL Projections
2021-09-29Leveraging Public Data for Practical Synthetic Data Generation
2021-07-13Efficient Exploration in Bayesian Optimization – Optimism and Beyond by Andreas Krause
2021-07-13Learning to Explore in Molecule Space by Yoshua Bengio
2021-07-13Resource Allocation in Multi-armed Bandits by Kirthevasan Kandasamy
2021-07-13Grey-box Bayesian Optimization by Peter Frazier
2021-06-10Is There a Mathematical Model of the Mind? (Panel Discussion)
2021-06-04Dataset Poisoning on the Industrial Scale
2021-06-04Federated Design of Compact and Private DNNs
2021-06-04Orchard: Differentially Private Analytics at Scale
2021-06-04Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogenous Data
2021-06-04Workshop on Federated Learning and Analytics: Pre-recorded Talks Day 2 Track 2 Q&A Privacy/Security
2021-06-04Workshop on Federated Learning & Analytics: Pre-recorded Talks Day 2 Track 1 Q&A Optimization/System
2021-06-04Generative Models for Effective ML on Private, Decentralized Datasets
2021-06-04Learning discrete distributions: User vs item-level privacy
2021-06-04Learning on Large-Scale Data with Security & Privacy
2021-06-04FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning
2021-06-04Profile-based Privacy for Locally Private Computations
2021-06-04Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning