FedGS: Federated Graph-Based Sampling with Arbitrary Client Availability
Authors: Zheng Wang, Xiaoliang Fan, Jianzhong Qi, Haibing Jin, Peizhen Yang, Siqi Shen, Cheng Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate the effectiveness of FEDGS, we conduct experiments on three datasets under a comprehensive set of seven client availability modes. Our experimental results confirm FEDGS s advantage in both enabling a fair client-sampling scheme and improving the model performance under arbitrary client availability. |
| Researcher Affiliation | Academia | 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: Federated Graph-Based Sampling |
| Open Source Code | Yes | Our code is available at https://github.com/Ww Zzz/Fed GS. |
| Open Datasets | Yes | We validate FEDGS on three commonly used federated datasets: Synthetic (0.5, 0.5) (Li et al. 2020), CIFAR10 (Krizhevsky and Hinton 2009) and Fashion MNIST (Xiao, Rasul, and Vollgraf 2017). |
| Dataset Splits | Yes | For each dataset, we tune the hyper-parameters by grid search with Fed Avg, and we adopt the optimal parameters on the validation dataset of Fed Avg to all the methods. |
| Hardware Specification | Yes | All our experiments are run on a 64 GBRAM Ubuntu 18.04.6 server with 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 1.12.0. |
| Experiment Setup | Yes | The batch size is B = 10 for Synthetic and B = 32 for both CIFAR10 and Fashion MNIST. The optimal parameters for Synthetic, Cifar10, Fashion MNIST are resepctively η = 0.1, E = 10, E = 10, η = 0.03 and E = 10, η = 0.1. We round-wisely decay the learning srate by a factor of 0.998 for all the datasets. |