Search Results for author: Dean Steuer

Found 2 papers, 1 papers with code

SEEC: Semantic Vector Federation across Edge Computing Environments

no code implementations • 30 Aug 2020 • Shalisha Witherspoon, Dean Steuer, Graham Bent, Nirmit Desai

Specifically, for scenarios where multiple edge locations can engage in joint learning, we adapt the recently proposed federated learning techniques for semantic vector embedding.

Edge-computing Federated Learning +3

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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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