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..
Federated-EM with heterogeneity mitigation and variance reduction
Authors: Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results are presented to support our theoretical findings, as well as an application to federated missing values imputation for biodiversity monitoring. |
| Researcher Affiliation | Academia | Aymeric Dieuleveut Centre de Math ematiques Appliqu ees Ecole Polytechnique, France Institut Polytechnique de Paris EMAIL Gersende Fort Institut de Math ematiques de Toulouse Universit e de Toulouse; CNRS EMAIL Eric Moulines Centre de Math ematiques Appliqu ees Ecole Polytechnique, France CS Dpt, HSE University, Russian Federation EMAIL Genevi eve Robin Laboratoire de Math ematiques et Mod elisation d Evry Universit e d Evry Val d Essonne; CNRS Evry-Courcouronnes, France EMAIL |
| Pseudocode | Yes | Algorithm 1: Fed EM with partial participation and Algorithm 2: VR-Fed EM |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | MNIST Data set. We perform a similar experiment on the MNIST dataset to illustrate the behaviour of Fed EM and VR-Fed EM on a GMM inference problem with real data. We apply Fed Miss EM to the analysis of part of the e Bird data base [34, 1] of field observations reported in France by I = 2,465 observers, across J = 9,721 sites and at L = 525 monthly time points. |
| Dataset Splits | No | The paper describes data distribution across servers and minibatch usage but does not explicitly provide training/validation/test dataset splits or mention a 'validation' set. |
| Hardware Specification | No | The paper mentions 'edge devices' and 'central server' but does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | For Fed EM, we consider the finite-sum setting when si = m^-1 Pm j=1 sij with m = 10^2; the oracle Sk+1,i is obtained by a sum over a minibatch of b = 20 examples. For VR-Fed EM, we set b = 5 and kin = 20. We run the two algorithms for 500 epochs (one epoch corresponds to N conditional expectation evaluations sij). We choose b = 20 and the step size is constant and set to γ = 10^-3. We run Fed Miss EM for 150 epochs; with γ = 10^-4, = 10^-3, b = 10^2, a rank r = 2 and λ = 0; for the distribution of the variables Xi j , we use a Gaussian distribution with unknown expectation j and variance 1. |