FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance
Authors: Zibin Pan, Shuyi Wang, Chi Li, Haijin Wang, Xiaoying Tang, Junhua Zhao
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experiments in several FL scenarios verify that Fed MDFG is robust and outperforms the SOTA FL algorithms in convergence and fairness. We evaluate the performance of Fed MDFG on four public datasets: CIFAR-10, CIFAR-100 (Krizhevsky and Hinton 2009), MNIST(Le Cun et al. 1998), and Fashion MNIST (FMNIST) (Xiao, Rasul, and Vollgraf 2017) |
| Researcher Affiliation | Academia | 1 The School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China 2 The Shenzhen Institute of Artificial Intelligence and Robotics for Society 3 The Guangdong Provincial Key Laboratory of Future Networks of Intelligence |
| Pseudocode | Yes | Algorithm 1: Fed MDFG |
| Open Source Code | Yes | The source code is available at https://github.com/zibinpan/Fed MDFG. |
| Open Datasets | Yes | We evaluate the performance of Fed MDFG on four public datasets: CIFAR-10, CIFAR-100 (Krizhevsky and Hinton 2009), MNIST(Le Cun et al. 1998), and Fashion MNIST (FMNIST) (Xiao, Rasul, and Vollgraf 2017), where the training/testing data are already split. |
| Dataset Splits | No | We evaluate the performance of Fed MDFG on four public datasets: CIFAR-10, CIFAR-100 (Krizhevsky and Hinton 2009), MNIST(Le Cun et. al. 1998), and Fashion MNIST (FMNIST) (Xiao, Rasul, and Vollgraf 2017), where the training/testing data are already split. |
| Hardware Specification | Yes | All experiments are implemented on a server with Intel(R) Xeon(R) Silver 4216 CPUs and 2 NVidia(R) 3090 GPUs. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Table 1 list the hyper-parameters of various techniques we verify. The first one of each parameter set is regarded as the default for each algorithm. All clients utilize Stochastic Gradient Descent (SGD) on local datasets with the learning rate η {0.01, 0.05, 0.1} and decay of 0.999 per round, and take the best performance of each method in comparison. We average the results in 5 runs with different random seeds. |