V
Vladimir Ivanov
Researcher at Google
Publications - 6
Citations - 4680
Vladimir Ivanov is an academic researcher from Google. The author has contributed to research in topics: Overhead (computing) & Mobile device. The author has an hindex of 5, co-authored 5 publications receiving 2924 citations.
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Practical Secure Aggregation for Privacy-Preserving Machine Learning
Keith Bonawitz,Vladimir Ivanov,Ben Kreuter,Antonio Marcedone,H. Brendan McMahan,Sarvar Patel,Daniel Ramage,Aaron Segal,Karn Seth +8 more
TL;DR: In this paper, the authors proposed a secure aggregation of high-dimensional data for federated deep neural networks, which allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner without learning each user's individual contribution.
Posted Content
Towards Federated Learning at Scale: System Design
Keith Bonawitz,Hubert Eichner,Wolfgang Grieskamp,Dzmitry Huba,Alex Ingerman,Vladimir Ivanov,Chloe Kiddon,Jakub Konečný,Stefano Mazzocchi,H. Brendan McMahan,Timon Van Overveldt,David Petrou,Daniel Ramage,Jason Roselander +13 more
TL;DR: In this paper, a scalable production system for federated learning in the domain of mobile devices, based on TensorFlow, is presented. Butler et al. describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.
Towards Federated Learning at Scale: System Design
Keith Bonawitz,Hubert Eichner,Wolfgang Grieskamp,Dzmitry Huba,Alex Ingerman,Vladimir Ivanov,Chloe Kiddon,Jakub Konečný,Stefano Mazzocchi,H. Brendan McMahan,Timon Van Overveldt,David Petrou,Daniel Ramage,Jason Roselander +13 more
TL;DR: A scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow is built, describing the resulting high-level design, and sketch some of the challenges and their solutions.
Posted Content
Practical Secure Aggregation for Privacy Preserving Machine Learning.
Keith Bonawitz,Vladimir Ivanov,Ben Kreuter,Antonio Marcedone,H. Brendan McMahan,Sarvar Patel,Daniel Ramage,Aaron Segal,Karn Seth +8 more
TL;DR: This protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner, and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network.
Posted Content
Practical Secure Aggregation for Federated Learning on User-Held Data
Keith Bonawitz,Vladimir Ivanov,Ben Kreuter,Antonio Marcedone,H. Brendan McMahan,Sarvar Patel,Daniel Ramage,Aaron Segal,Karn Seth +8 more
TL;DR: This work considers training a deep neural network in the Federated Learning model, using distributed stochastic gradient descent across user-held training data on mobile devices, wherein Secure Aggregation protects each user's model gradient.