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..
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning
Authors: Antonious Girgis, Deepesh Data, Suhas Diggavi
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We numerically demonstrate that, for important regimes, with composition our bound yields significant improvement in privacy guarantee over the state-of-the-art approximate Differential Privacy (DP) guarantee (with strong composition) for sub-sampled shuffled models. We also demonstrate numerically significant improvement in privacy-learning performance operating point using real data sets. |
| Researcher Affiliation | Academia | Antonious M. Girgis UCLA EMAIL Deepesh Data UCLA EMAIL Suhas Diggavi UCLA EMAIL |
| Pseudocode | Yes | Algorithm 1 Acldp: CLDP-SGD |
| Open Source Code | No | The paper does not provide a link to open-source code for the methodology, nor does it explicitly state that the code is being released or is available in supplementary materials. |
| Open Datasets | Yes | We consider the standard MNIST handwritten digit dataset that has 60, 000 training images and 10, 000 test images. |
| Dataset Splits | No | The paper mentions '60,000 training images and 10,000 test images' for the MNIST dataset but does not specify a separate validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | At each step of the Algorithm 1, we choose uniformly at random 10, 000 clients, where each client clips the 1-norm of the gradient with clipping parameter C = 1/100 and applies the R1 0-LDP mechanism proposed in [27] with 0 = 1.5. We run Algorithm 1 with δ = 10 5 for 200 epochs, with learning rate = 0.3 for the first 70 epochs, and then decrease it to 0.18 in the remaining epochs. |