Distributed Point Functions: Efficient Secure Aggregation and Beyond with Non-Colluding Servers

Subscribers:
348,000
Published on ● Video Link: https://www.youtube.com/watch?v=E9s2MbXuHx8



Duration: 7:06
492 views
3


A Google TechTalk, presented by Phillipp Schoppmann, Google, at the 2021 Google Federated Learning and Analytics Workshop, Nov. 8-10, 2021.

For more information about the workshop: https://events.withgoogle.com/2021-workshop-on-federated-learning-and-analytics/#content




Other Videos By Google TechTalks


2022-05-052022 Blockly Developers Summit: Contributing to Blockly
2022-05-052022 Blockly Developers Summit: Backwards Execution
2022-02-14Probabilistic Numerics — moving BayesOpt expertise to the inner loop by Philipp Hennig
2022-02-08Information-Constrained Optimization: Can Adaptive Processing of Gradients Help?
2022-02-08Differential privacy dynamics of noisy gradient descent
2022-02-08Consistent Spectral Clustering of Network Block Models under Local Differential Privacy
2022-02-08The Skellam Mechanism for Differentially Private Federated Learning
2022-02-08Statistical Heterogeneity in Federated Learning
2022-02-08Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning
2022-02-08Tight Accounting in the Shuffle Model of Differential Privacy
2022-02-08Distributed Point Functions: Efficient Secure Aggregation and Beyond with Non-Colluding Servers
2022-02-08How to Turn Privacy ON and OFF and ON Again
2022-02-08Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
2022-02-08Secure Federated Learning on Wimpy Devices
2022-02-08Gaps between FL optimization theory and practice
2022-02-08Mistify: Automating DNN Model Porting for On-Device Inference at the Edge
2022-02-08Personalized Graph-Aided Online Federated Model Selection
2022-02-08Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition
2022-02-08Distributed neural network training via independent subnets
2022-02-08CaPC Learning: Confidential and Private Collaborative Learning
2022-02-08Locally Differentially Private Bayesian Inference