no code implementations • 7 Apr 2022 • Yonghai Gong, Yichuan Li, Nikolaos M. Freris
Federated Learning (FL) is an emerging framework for distributed processing of large data volumes by edge devices subject to limited communication bandwidths, heterogeneity in data distributions and computational resources, as well as privacy considerations.
no code implementations • 5 Dec 2021 • Jiawei Huang, Ruomin Huang, Wenjie Liu, Nikolaos M. Freris, Hu Ding
A wide range of optimization problems arising in machine learning can be solved by gradient descent algorithms, and a central question in this area is how to efficiently compress a large-scale dataset so as to reduce the computational complexity.
no code implementations • 22 Apr 2018 • Saif Eddin Jabari, Nikolaos M. Freris, Deepthi Mary Dilip
The second shortcoming is the wide-spread use of Gaussian probability densities as mixture components.
no code implementations • 22 Mar 2018 • Hoi-To Wai, Nikolaos M. Freris, Angelia Nedic, Anna Scaglione
We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems.
no code implementations • 17 Dec 2013 • Nikolaos M. Freris, Orhan Öçal, Martin Vetterli
We introduce a recursive algorithm for performing compressed sensing on streaming data.