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..
Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching
Authors: Wonyong Jeong, Sung Ju Hwang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively validate our method on both label and domain-heterogeneous settings, on which it outperforms the state-of-the-art personalized federated learning methods. The code is available at https://github.com/wyjeong/Factorized-FL. 5 Experiment |
| Researcher Affiliation | Academia | Wonyong Jeong Graduate School of AI KAIST, Seoul, South Korea EMAIL Sung Ju Hwang Graduate School of AI KAIST, Seoul, South Korea EMAIL |
| Pseudocode | Yes | As for the full training procedure, please see the pseudo-code of the algorithm in the supplementary file (Section A). |
| Open Source Code | Yes | The code is available at https://github.com/wyjeong/Factorized-FL. |
| Open Datasets | Yes | Datasets (1) Label Heterogeneous Scenario: we use CIFAR-10 [11] and SVHN [21] datasets... (2) Domain Heterogeneous Scenario: we use CIFAR-100 datasets [11] |
| Dataset Splits | No | The paper mentions splitting datasets into partitions for clients and using test accuracy, but it does not explicitly provide the specific train/validation/test dataset splits (e.g., percentages or sample counts) used for reproduction. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or cloud computing instances used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., 'Python 3.x', 'PyTorch x.x') required to replicate the experiments. |
| Experiment Setup | Yes | Top (label heterogeneous scenario): we train 20 clients on each dataset (CIFAR-10 & SVHN) for 250 training iterations (E=5, R=50). Bottom (domain & label heterogeneous scenario): We train 20 clients for 500 training iterations (E=5,R=100)... |