This is work has been submitted to the IEEE Transactions on Network and Service Management, Special Issue on "Research Advances Towards Effective and Sustainable Next Generation Networks".
This repository contains a discrete-event simulator capable of simulating Gossip Learning (GL) with various network topologies and stopping criteria. Moreover, it contains all the experimental material for reproducing the empirical evaluation of the paper.
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├── src
└── experiments
It contains the code of the Python simulator package, utility scripts to run a simulation, and numbered Jupyter Notebooks step-by-step perform an end-to-end simulation (e.g., from dataset preparation to evaluation).
It contains all the data used for experiments, utility scripts for the analysis of the simulation results, and for plot generation.
Distributed under the GPL v3 license. See LICENSE for more information.