Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching
Authors: Wonyong Jeong, Sung Ju Hwang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 wyjeong@kaist.ac.kr Sung Ju Hwang Graduate School of AI KAIST, Seoul, South Korea sjhwang82@kaist.ac.kr |
| 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)... |