MUSKETEER H2020 Project
Published August 23, 2021 | Version 1.0
Project deliverable Open

D6.2 Scalability of machine learning algorithms over every POMs

Authors/Creators

  • 1. Universidad Carlos III de Madrid
  • 2. Tree Technology

Contributors

Project leader:

  • 1. COMAU
  • 2. ENGINEERING
  • 3. IBM

Description

This deliverable (D6.2 Scalability of machine learning algorithms over every POMs) is the result of task T6.2 (Assessing scalability and computational efficiency of federated privacy-preserving machine learning algorithms) and describes the results obtained after intensive tests carried out to analyse the main technical characteristics of every algorithm over every POM. We have followed the Goal Question Metric methodology described in D6.1, and the main goals are to assess the scalability, computational efficiency and performance of the proposed and implemented schemes in the MUSKETEER Machine Learning Library (MMLL).

Files

MUSKETEER_D6.2.pdf

Files (21.0 MB)

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Additional details

Funding

European Commission
MUSKETEER - Machine learning to augment shared knowledge in federated privacy-preserving scenarios 824988