FLIPS: Federated Learning using Intelligent Participant Selection

no code implementations • 7 Aug 2023 • Rahul Atul Bhope, K. R. Jayaram, Nalini Venkatasubramanian, Ashish Verma, Gegi Thomas

In particular, we examine the benefits of label distribution clustering on participant selection in federated learning.

Clustering Federated Learning +1

MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent

no code implementations • 1 Dec 2020 • K. R. Jayaram, Archit Verma, Ashish Verma, Gegi Thomas, Colin Sutcher-Shepard

Federated learning enables multiple, distributed participants (potentially on different clouds) to collaborate and train machine/deep learning models by sharing parameters/gradients.

Federated Learning

IBM Federated Learning: an Enterprise Framework White Paper V0.1

1 code implementation • 22 Jul 2020 • Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Whitherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, Annie Abay

Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.

BIG-bench Machine Learning Federated Learning

NeuNetS: An Automated Synthesis Engine for Neural Network Design

no code implementations • 17 Jan 2019 • Atin Sood, Benjamin Elder, Benjamin Herta, Chao Xue, Costas Bekas, A. Cristiano I. Malossi, Debashish Saha, Florian Scheidegger, Ganesh Venkataraman, Gegi Thomas, Giovanni Mariani, Hendrik Strobelt, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati

Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.