Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. This approach stands in contrast to traditional centralized machine learning techniques where all the local datasets are uploaded to one server, as well as to more classical decentralized approaches which often assume that local data samples are identically distributed. Federated learning enables multiple actors to build a common, robust machine learning model without sharing data, thus allowing to address critical issues such as data privacy, data security, data access rights and access to heterogeneous data. Its applications are spread over a number of industries including defense, telecommunications, IoT, and pharmaceutics. A major open question at the moment is how inferior models learned through federated data are relative to ones where the data are pooled. Another open question concerns the trustworthiness of the edge devices and the impact of malicious actors on the learned model. (Wikipedia).
[deep learning] Federated Learning - training on decentralized data
Understanding Federated Learning. In order to secure the privacy of data, Federated Learning leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. all machine learning youtube videos from me, https://www.youtube.com/
From playlist Machine Learning
Adaptive Federated Optimization
A Google TechTalk, 2020/7/30, presented by Zachary Charles, Google ABSTRACT:
From playlist 2020 Google Workshop on Federated Learning and Analytics
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Machine Learning
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
A Google TechTalk, presented by Aritra Mitra, University of Pennsylvania, 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
From playlist 2021 Google Workshop on Federated Learning and Analytics
Federated learning with only positive labels and federated deep retrieval
A Google TechTalk, 2020/7/30, presented by Felix Yu, Google ABSTRACT:
From playlist 2020 Google Workshop on Federated Learning and Analytics
Workshop on Federated Learning & Analytics: Pre-recorded Talks Day 1 Track 2 Q&A Privacy/Security
A Google TechTalk, 2020/7/29, presented by all Day Track 2 speakers ABSTRACT: Google Workshop on Federated Learning and Analytics: Pre-recorded Talks Day 1 Track 2 Question and Answer session on Privacy/Security
From playlist 2020 Google Workshop on Federated Learning and Analytics
Federated Multi-Task Learning under a Mixture of Distributions
A Google TechTalk, presented by Aurélien Bellet, INRIA, 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
From playlist 2021 Google Workshop on Federated Learning and Analytics
Academic Keynote: Federated Learning with Strange Gradients, Martin Jaggi (EPFL)
A Google TechTalk, presented by Martin Jaggi, 2021/11/8 ABSTRACT: Federated Learning with Strange Gradients. Collaborative learning methods such as federated learning are enabling many promising new applications for machine learning while respecting users' privacy. In this talk, we discus
From playlist 2021 Google Workshop on Federated Learning and Analytics
Salvaging Federated Learning: Google Workshop on Federated Learning and Analytics Day 1 Keynote, Q&A
A Google TechTalk, 2020/7/29, presented by Vitaly Shmatikov, Cornell Tech ABSTRACT:
From playlist 2020 Google Workshop on Federated Learning and Analytics
Federated Learning and Analytics Research Using TensorFlow Federated
A Google TechTalk, presented by Google TFF Researchers, 2021/11/10 ABSTRACT: Sometimes centrally collecting data produced by edge devices, such as mobile phones, wearables, or cars, is infeasible or undesirable. With federated learning and analytics, clients collaboratively train a model o
From playlist 2021 Google Workshop on Federated Learning and Analytics
Flower: A Friendly Federated Learning Framework
A Google TechTalk, 2020/7/29, presented by Nicholas Lane, University of Cambridge. ABSTRACT: Full title: Flower: A Friendly Federated Learning Framework .. and a first look into the carbon footprint of federated methods Federated Learning (FL) has emerged as a promising technique for edg
From playlist 2020 Google Workshop on Federated Learning and Analytics
TensorFlow Federated Tutorial Session
A Google TechTalk, 2020/7/31, presented by Google Research Staff ABSTRACT:
From playlist 2020 Google Workshop on Federated Learning and Analytics
On Heterogeneity in Federated Settings: Workshop on Federated Learning and Analytics Day 2 Keynote
A Google TechTalk, 2020/7/30, presented by Virginia Smith, Carnegie Mellon University ABSTRACT:
From playlist 2020 Google Workshop on Federated Learning and Analytics
Google Keynote: Federated Aggregation and Privacy
A Google TechTalk, presented by Dan Ramage, Brendan McMahan, & Kallista Bonawitz, 2021/11/8 ABSTRACT: 3 Google researchers talk about the state of the art in federated aggregations and privacy. About the Speakers Brendan McMahan, Google - Brendan McMahan has worked in the fields of onlin
From playlist 2021 Google Workshop on Federated Learning and Analytics
Welcome and Federated Learning and Analytics at Google
A Google TechTalk, presented by Sean Augenstein and Brendan McMahan, 2022/11/09. Presented at the 2022 Workshop on Federated Learning and Analytics. About the speaker: Bio: Brendan McMahan is a research scientist at Google, where he leads efforts on decentralized and privacy-preserving ma
From playlist 2022 Workshop on Federated Learning and Analytics
Google Keynote: Federated Learning & Federated Analytics-Research, Applications, & System Challenges
A Google TechTalk, presented by Hubert Eichner, Francoise Beaufay, Ravi Kumar & Peter Kairouz, 2021/11/9 ABSTRACT: An overview of federated analytics applications and algorithms, federated learning applications and algorithms, and how we build an infrastructure and scale it. About the S
From playlist 2021 Google Workshop on Federated Learning and Analytics
This video is part of the Udacity course "Deep Learning". Watch the full course at https://www.udacity.com/course/ud730
From playlist Deep Learning | Udacity