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..
Personalized Federated Learning via Feature Distribution Adaptation
Authors: Connor Mclaughlin, Lili Su
NeurIPS 2024 | Venue PDF | 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 EMAIL |
| 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. |