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..
On the Privacy-Robustness-Utility Trilemma in Distributed Learning
Authors: Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section E.1, we present our experimental setup. In Section E.2, we report our empirical results. |
| Researcher Affiliation | Academia | 1Ecole Polytechnique F ed erale de Lausanne (EPFL), Switzerland. |
| Pseudocode | Yes | Algorithm 1 SAFE-DSHB |
| Open Source Code | No | The code we use to launch the different experiments will be made available. |
| Open Datasets | Yes | We train a logistic regression model of d = 69 parameters on the academic Phishing5 dataset. (footnote 5: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/) |
| Dataset Splits | No | The paper mentions using the Phishing dataset and shows 'Test accuracy' and 'Training Loss' but does not specify the dataset splits (e.g., train/validation/test percentages or counts) or the methodology used for splitting. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | We use Opacus (Yousefpour et al., 2021), a DP library for deep learning in Py Torch (Paszke et al., 2019). |
| Experiment Setup | Yes | We train the model using a fixed learning rate γ = 1 over a total of T = 400 learning steps. We set the clipping threshold C = 1 and the batch size b = 25. We run all algorithms, except DSGD, with momentum β = 0.99. |