Search Results for author: Shashank Rajamoni

Found 2 papers, 1 papers with code

Mitigating Bias in Federated Learning

no code implementations • 4 Dec 2020 • Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, Heiko Ludwig

As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored.

Fairness Federated Learning

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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

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