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 [1].
Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
Authors: Meirui Jiang, Anjie Le, Xiaoxiao Li, Qi Dou
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have theoretically analyzed the convergence of our method in the over-parameterize regime, and experimentally evaluated our method on five datasets. These datasets present heterogeneous data features in natural and medical images. With comprehensive comparison to existing state-of-the-art approaches, our LG-Mix has consistently outperformed them across all datasets (largest accuracy improvement of 5.01%), demonstrating the outstanding efficacy of our method for model personalization. |
| Researcher Affiliation | Academia | Meirui Jiang Department of Computer Science and Engineering The Chinese University of Hong Kong EMAIL Anjie Le Department of Computer Science and Engineering The Chinese University of Hong Kong EMAIL Xiaoxiao Li Department of Electrical and Computer Engineering The University of British Columbia EMAIL Qi Dou Department of Computer Science and Engineering The Chinese University of Hong Kong EMAIL |
| Pseudocode | Yes | Algorithm 1: LG-Mix: Local-global updates mixing algorithm |
| Open Source Code | Yes | Code is available at https://github.com/med-air/Hetero PFL. |
| Open Datasets | Yes | Digits-5 (Zhou et al., 2020; Li et al., 2021c) ... Office-Caltech10 (Gong et al., 2012) ... Domain Net (Peng et al., 2019) ... Camelyon17 (Bandi et al., 2018) ... Retinal dataset which contains retinal fundus images from 6 different sources (Fumero et al., 2011; Sivaswamy et al., 2015; Almazroa et al., 2018; Orlando et al., 2020). |
| Dataset Splits | Yes | For all datasets, we take each data source as one client and split the data of each client into train, validation, and testing sets with a ratio of 0.6, 0.2, and 0.2. |
| Hardware Specification | Yes | The GPU we used for training is Ge Force RTX 2080 Ti. |
| Software Dependencies | No | The paper mentions software like "Py Torch" but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We use the SGD optimizer with a learning rate of 0.01 and Cross Entropy loss for classification tasks and use Adam optimizer with learning rate of 1e 3 with β = (0.9, 0.99), dice loss (Milletari et al., 2016) for the segmentation task. ... The total number of training rounds is 100 with a local update epoch of 1. ... All input images are resized to 28 28. |