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..
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning
Authors: Jun Luo, Shandong Wu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our method s convergence and generalization behaviors, and perform extensive experiments on two benchmark datasets and two medical imaging datasets under two non-IID settings. |
| Researcher Affiliation | Academia | Jun Luo1 , Shandong Wu1,2,3,4 1Intelligent Systems Program, University of Pittsburgh 2Department of Radiology, University of Pittsburgh 3Department of Biomedical Informatics, University of Pittsburgh 4Department of Bioengineering, University of Pittsburgh EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 APPLE |
| Open Source Code | Yes | The code is publicly available at https: //github.com/ljaiverson/p FL-APPLE. |
| Open Datasets | Yes | Datasets. We use four public datasets including two benchmark datasets: MNIST and CIFAR10, and two medical imaging datasets from the Med MNIST datasets collection [Yang et al., 2021], namely the Organ MNIST(axial) dataset: an 11-class of liver tumor image dataset, and the Path MNIST dataset: a 9-class colorectal cancer image dataset. |
| Dataset Splits | No | The paper mentions partitioning datasets into a "training set and a test set" and discusses training for a certain number of "rounds" and "local epochs," but it does not explicitly provide details about a validation set split (e.g., percentages or counts). |
| Hardware Specification | No | The paper mentions using "the Bridges-2 system... at the Pittsburgh Supercomputing Center" but does not specify any particular CPU models, GPU models, or detailed hardware configurations (e.g., memory, specific processor types) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We train each method 160 rounds with 5 local epochs and summarize the results as follows. In Equation 7, ฮป is a dynamic function ranging from 0 and 1, with respect to the round number, r, and ยต is a scalar coefficient for the proximal term. More details regarding the loss scheduler are presented in Appendix A.1. |