Federated Learning with Fair Averaging
Authors: Zheng Wang, Xiaoliang Fan, Jianzhong Qi, Chenglu Wen, Cheng Wang, Rongshan Yu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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}. |