Project Details
Project Description
Federated Learning (FL) enables mobile phones to collaboratively learnashared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. This goes beyond the use of local models that make predictions on mobile devices by bringing model training to the device as well. FL was co-invented by my former student Jakub Konecny, myself and Google.
We have launched. a FL system in 2017, it is now in use in more than 1 billion Android devices:
https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
https://ai.google/research/pubs/pub45648
In this project we will investigate further improvements and applications of FL.
About the Researcher
Affiliations
Education Profile
- PhD, Operations Research, Cornell University, 2007
- MS, Operations Research, Cornell University, 2006
- Mgr, Mathematics, Comenius University, 2001
- Bc, Management Comenius University, 2001
- Bc, Mathematics, Comenius University, 2000
Research Interests
Prof. Richtarik's research interests lie at the intersection of mathematics, computer science, machine learning, optimization, numerical linear algebra, high performance computing and applied probability. He is interested in developing zero, first, and second-order algorithms for convex and nonconvex optimization problems described by big data, with a particular focus on randomized, parallel and distributed methods. He is the co-inventor of federated learning, a Google platform for machine learning on mobile devices preserving privacy of users' data.Selected Publications
- R. M. Gower, D. Goldfarb and P. Richtarik. Stochastic block BFGS: squeezing more curvature out of data, Proceedings of The 33rd International Conference on Machine Learning, pp. 1869-1878, 2016
- J. Konecny, J. Liu, P. Richtarik and M. Takac. Mini-batch semi-stochastic gradient descent in the proximal setting, IEEE Journal of Selected Topics in Signal Processing 10(2), 242a-255, 2016
- P. Richtarik and M. Takac. Parallel coordinate descent methods for big data optimization Mathematical Programming 156(1):433a-484, 2016
- R. M. Gower and P. Richtarik. Randomized iterative methods for linear systems, SIAM Journal on Matrix Analysis and Applications 36(4):1660-1690, 2015
- O. Fercoq and P. Richtarik. Accelerated, parallel and proximal coordinate descent. SIAM Journal on Optimization 25(4):1997a-2023, 2015
Desired Project Deliverables
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