Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 58,592 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,217 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |