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..
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
Authors: Edwige Cyffers, Mathieu Even, Aurélien Bellet, Laurent Massoulié
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we illustrate our privacy gains with experiments on synthetic and real-world datasets. ... We demonstrate the usefulness of our approach and analysis through experiments on synthetic and real-world datasets and network graphs. |
| Researcher Affiliation | Academia | 1Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 CRISt AL, F-59000 Lille 2Inria Paris Département d informatique de l ENS, PSL Research University, Paris, France |
| Pseudocode | Yes | Algorithm 1: MUFFLIATO ... Algorithm 2: RANDOMIZED MUFFLIATO ... Algorithm 3: MUFFLIATO-SGD and MUFFLIATO-GD |
| Open Source Code | Yes | The code used to obtain these results is available at https://github.com/totilas/muffliato. |
| Open Datasets | Yes | We use a binarized version of UCI Housing dataset.3 [footnote: 3https://www.openml.org/d/823] ... We consider the graphs of the Facebook ego dataset [41] |
| Dataset Splits | Yes | We split the dataset uniformly at random into a training set (80%) and a test set |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., Python, PyTorch, specific libraries). |
| Experiment Setup | Yes | Input: initial points θ0 v RD, number of iterations T, step sizes ν > 0, noise variance σ2, gossip matrices (Wt)t 0, local functions ϕv, number of communication rounds K ... After each gradient step of Muffliato-GD, we draw at random an Erdös-Rényi graph of same parameter q to perform the gossiping step and run the theoretical number of steps required for convergence. |