SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning
Authors: Tanguy Marchand, Boris Muzellec, Constance Béguier, Jean Ogier du Terrail, Mathieu Andreux
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative experiments on real data demonstrate that, in addition to being secure, our approach reliably normalizes features across silos as well as if data were pooled, making it a viable approach for safe federated feature Gaussianization. |
| Researcher Affiliation | Industry | Tanguy Marchand Owkin Inc., New York, USA. tanguy.marchand@owkin.com Boris Muzellec Owkin Inc., New York, USA. boris.muzellec@owkin.com Constance Beguier Jean Ogier du Terrail Owkin Inc., New York, USA. jean.du-terrail@owkin.com Mathieu Andreux Owkin Inc., New York, USA. mathieu.andreux@owkin.com |
| Pseudocode | Yes | Algorithm 1 EXPYJ ... Algorithm 2 EXPUPDATE ... Algorithm 3 SECUREFEDYJ |
| Open Source Code | No | The code of the experiments is not provided, but a detailed pseudo-code of the newly proposed algorithms are provided. |
| Open Datasets | Yes | All datasets used in this work are publicly available from the UCI machine learning repository [15]. ... [15] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | Yes | We evaluate each strategy using 5-fold cross-validation with 5 different seeds. |
| Hardware Specification | No | The experiment are not heavy and run easily on a personal computer, on a CPU |
| Software Dependencies | No | We implement SECUREFEDYJ in Python, using the MPy C library [42] based on Shamir Secret Sharing [44]. ... Standard implementations of the YJ transformation, in particular the scikit-learn implementation [36]... |
| Experiment Setup | Yes | We specified all the hyperparameters and the details of the numerical experiment in Appendix E. ... For the Cox PH model, we train it with L1 regularization (alpha = 0.5) using the CPHNN-L1 solver from the lifelines Python package. We select the learning rate among {1e-3, 1e-4, 1e-5} based on cross-validation on the training set. |