3 code implementations • 16 Apr 2024 • Hubert Eichner, Daniel Ramage, Kallista Bonawitz, Dzmitry Huba, Tiziano Santoro, Brett McLarnon, Timon Van Overveldt, Nova Fallen, Peter Kairouz, Albert Cheu, Katharine Daly, Adria Gascon, Marco Gruteser, Brendan Mcmahan
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data.
no code implementations • 8 Nov 2021 • Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, Kaikai Wang, Anthony Shoumikhin, Jesik Min, Mani Malek
Our work tackles the aforementioned issues, sketches of some of the system design challenges and their solutions, and touches upon principles that emerged from building a production FL system for millions of clients.
no code implementations • 11 Jun 2021 • John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek, Dzmitry Huba
On the other hand, asynchronous aggregation of client updates in FL (i. e., asynchronous FL) alleviates the scalability issue.
7 code implementations • 4 Feb 2019 • 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
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.