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..
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
Authors: Jinhyun So, Ramy E. Ali, Başak Güler, Jiantao Jiao, A. Salman Avestimehr
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on MNIST, CIFAR-10 and CIFAR100 datasets in the IID and the non-IID settings demonstrate the performance improvement over the baselines in terms of privacy protection and test accuracy. |
| Researcher Affiliation | Collaboration | Jinhyun So*1, Ramy E. Ali 1, Bas ak G uler2, Jiantao Jiao3, A. Salman Avestimehr1 1 University of Southern California (USC) 2 University of California, Riverside 3 University of California, Berkeley EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | We describe the two components of Multi-Round Sec Agg in detail in Algorithms 1 and 2 in (So et al. 2021b, App. D). |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described in this specific version. |
| Open Datasets | Yes | MNIST (Le Cun, Cortes, and Burges 2010), CIFAR-10, and CIFAR100 (Krizhevsky and Hinton 2009) |
| Dataset Splits | No | The paper describes how training samples are partitioned across users (IID and Non-IID settings) but does not explicitly provide details about train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU, CPU models, memory) used for conducting the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | The hyperparameters are provided in (So et al. 2021b, App. F). |