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researcher

C. Béguier

4 papers hereh-index 7395 citations7 works total

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

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • stat.ML2
  • cs.CV1
  • cs.LG1

identity via Semantic Scholar / OpenAlex

activity
20202022
most citedDifferentially Private Federated Learning for Cancer Prediction

16 citations · 30 across the 3 of their papers we have counts for

collaborators
Showing 2020Show all

2 papers · 1 filter

cs.CV2020

Siloed Federated Learning for Multi-Centric Histopathology Datasets

Mathieu Andreux, Jean Ogier du Terrail, Constance Beguier +1

While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially…

stat.ML2020★ 11 cited

Efficient Sparse Secure Aggregation for Federated Learning

Constance Beguier, Mathieu Andreux, Eric W. Tramel

Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets. In real-world settings, this approach is hindered by…

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