The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21572601

Dekker, Florine; Erkin, Zekeriya; Conti, Mauro (2025): Source code underlying the publication: Topology-Based Reconstruction Prevention for Decentralised Learning. Version 2. 4TU.ResearchData. software. https://doi.org/10.4121/21572601.v2

Software

x choose version: version 1 - 2023-12-18

• Artificial Intelligence and Image Processing
• Other Information and Computing Sciences
• Distributed Computing
• Information and Computing Sciences

decentralised learning, graph girth, privacy-preserving summation, reconstruction attacks, statistical disclosure

CC BY-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Florine Dekker , Zekeriya Erkin , Mauro Conti

MATLAB code to reproduce results presented in the paper "Topology-Based Reconstruction Prevention for Decentralised Learning".

The source code is available as a git repository. Cached computation results are available as additional files.

Git repository

The git repository has two parts:

* FeasibilitySim contains the source code of the experiments from Section 4.4. Its main script is FeasibilitySim.m.

* PerformanceSim contains the source code of the experiments from Section 5.5. Its main script is PerformanceSim.m.

The parts are described in more detail in the file README.md in their respective folders.

Detailed usage instructions for both parts are available in the file ARTIFACT-EVALUATION.md.

Files

Cached computation results are optionally available, with filenames ending in .cache.zip. These allow inspecting the exact outputs without the need to re-run the computationally intensive experiments. Detailed usage instructions are available in the file ARTIFACT-EVALUATION.md inside the git repository.

• 2023-12-18 first online

• 2025-01-13 published, posted

4TU.ResearchData

MATLAB code

Topology-Based Reconstruction Prevention for Decentralised Learning

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems