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 [1].
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. |