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researcher

Gegi Thomas

3 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cs.CR1

identity via Semantic Scholar / OpenAlex

most citedIBM Federated Learning: an Enterprise Framework White Paper V0.1

112 citations · 118 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CR2020

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

K. R. Jayaram, Archit Verma, Ashish Verma +2

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

cs.LG2020★ 112 cited

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

Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21

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. How…

cs.LG2019★ 6 cited

NeuNetS: An Automated Synthesis Engine for Neural Network Design

Atin Sood, Benjamin Elder, Benjamin Herta +17

Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through AP…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.