Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication
Authors: Martijn De Vos, Sadegh Farhadkhani, Rachid Guerraoui, Anne-marie Kermarrec, Rafael Pires, Rishi Sharma
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate EL in a 96-node network and compare its performance with state-of-the-art DL approaches. Our results illustrate that EL converges up to 1.7 quicker than baseline DL algorithms and attains 2.2% higher accuracy for the same communication volume. Our theoretical analysis in Section 3 shows that EL surpasses the best-known static and randomized topologies in terms of convergence speed. |
| Researcher Affiliation | Academia | Martijn de Vos Sadegh Farhadkhani Rachid Guerraoui Anne-Marie Kermarrec Rafael Pires Rishi Sharma EPFL, Switzerland |
| Pseudocode | Yes | Algorithm 1 Epidemic Learning as executed by a node i |
| Open Source Code | Yes | Source code can be found at https://github.com/sacs-epfl/decentralizepy/releases/tag/epidemic-neurips-2023. |
| Open Datasets | Yes | We evaluate the baseline algorithms using the CIFAR-10 image classification dataset [26] and the FEMNIST dataset, the latter being part of the LEAF benchmark [7]. |
| Dataset Splits | No | The step size (γ) was tuned by running each baseline on a range of values and taking the setup with the best validation accuracy. |
| Hardware Specification | Yes | We perform experiments on 6 hyperthreading-enabled machines with dual Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz of 8 cores. |
| Software Dependencies | Yes | Both EL-Oracle and EL-Local were implemented using the Decentralize Py framework [11] and Python 3.83. |
| Experiment Setup | Yes | We deploy 96 DL nodes for each experiment, interconnected according to the evaluated topologies. When experimenting with s-regular topologies, each node maintains a fixed degree of log2(n), i.e., each node has 7 neighbors. ... The step size (γ) was tuned by running each baseline on a range of values and taking the setup with the best validation accuracy. The optimal step-sizes are γ = 0.1 for Fully connected and 8-U-Equi Static, and γ = 0.05 for the remaining algorithms and topologies over CIFAR-10. For FEMNIST, the optimal step size is γ = 0.1 for all algorithms and topologies. Table 3 provides a summary of the learning parameters, model, and dataset used in the experiments. (e.g. batch size (b)=8, training steps per round (r)=3 for CIFAR-10) |