Personalized Federated Learning via Feature Distribution Adaptation
Authors: Connor Mclaughlin, Lili Su
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive computer vision benchmarks, we demonstrate that our method can adjust to complex distribution shifts with significant improvements over current state-of-the-art in data-scarce settings. Our source code is available on Git Hub1. 5 Experiments |
| Researcher Affiliation | Academia | Connor J. Mc Laughlin, Lili Su Northeastern University, Boston, MA 02115 {mclaughlin.co,l.su}@northeastern.edu |
| Pseudocode | Yes | Algorithm 1: p Fed FDA |
| Open Source Code | Yes | Our source code is available on Git Hub1. 1https://github.com/cj-mclaughlin/p Fed FDA |
| Open Datasets | Yes | We consider image classification tasks and evaluate our method on four popular datasets. The EMNIST [4] dataset is for 62-class handwriting image classification. The CIFAR10/CIFAR100 [22] datasets are for 10 and 10-class color image classification. The Tiny Image Net [23] dataset is for 200-class natural image classification. |
| Dataset Splits | Yes | We split each client s data partition 80-20% between training and testing. For p Fed FDA, we use k = 2 cross-validation folds to estimate a single βi term for each client. |
| Hardware Specification | Yes | All experiments are implemented in Py Torch 2.1 [36] and were each trained with a single NVIDIA A100 GPU. |
| Software Dependencies | Yes | All experiments are implemented in Py Torch 2.1 [36] and were each trained with a single NVIDIA A100 GPU. |
| Experiment Setup | Yes | We train all algorithms with mini-batch SGD for E = 5 local epochs and R = 200 global rounds. We apply no data augmentation besides normalization into the range [ 1, 1]. For p Fed FDA, we use k = 2 cross-validation folds to estimate a single βi term for each client. Additional training details and hyperparameters for each baseline method are provided in Appendix C.2. |