Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Federated Learning with Fair Averaging
Authors: Zheng Wang, Xiaoliang Fan, Jianzhong Qi, Chenglu Wen, Cheng Wang, Rongshan Yu
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,183 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,035 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on a suite of federated datasets confirm that Fed FV compares favorably against state-of-the-art methods in terms of fairness, accuracy and efficiency. |
| Researcher Affiliation | Academia | Zheng Wang1 , Xiaoliang Fan1, , Jianzhong Qi2 , Chenglu Wen1 , Cheng Wang 1 and Rongshan Yu 1 1Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen, China 2School of Computing and Information Systems, University of Melbourne, Melbourne, Australia |
| Pseudocode | Yes | Algorithm 1 Fed FV Input:T, m, α, τ, η, θ0, pk, k = 1, ..., K, ... Algorithm 2 Mitigate Internal Conflict Input: POt, Gt, α ... Algorithm 3 Mitigate External Conflict Input: gt, GH, τ |
| Open Source Code | Yes | The source code is available at https://github.com/Ww Zzz/easy FL. |
| Open Datasets | Yes | We evaluate Fed FV on three public datasets: CIFAR-10 [Krizhevsky, 2012], Fashion MNIST [Xiao et al., 2017] and MNIST [Le Cun et al., 1998]. |
| Dataset Splits | Yes | The local dataset is split into training and testing data with percentages of 80% and 20%. |
| Hardware Specification | Yes | All our experiments are implemented on a 64g-MEM Ubuntu 16.04.6 server with 40 Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz and 4 NVidia(R) 2080Ti GPUs. |
| Software Dependencies | Yes | All code is implemented in Py Torch version 1.3.1. |
| Experiment Setup | Yes | For all experiments, we fix the local epoch E = 1 and use batchsize BCIF AR 10|MNIST {full, 64}, BF ashion MNIST {full, 400} to run Stochastic Gradient Descent (SGD) on local datasets with stepsize η {0.01, 0.1}. |