FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment
Authors: Jiahao Liu, Jiang Wu, Jinyu Chen, Miao Hu, Yipeng Zhou, Di Wu
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct extensive experiments using five real datasets and the results demonstrate that Fed DWA can significantly reduce the communication traffic and achieve much higher model accuracy than the state-of-the-art approaches. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China 2Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China 3School of Computing, Faculty of Science and Engineering, Macquarie University, Sydney, Australia |
| Pseudocode | Yes | Algorithm 1 Fed DWA algorithm |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the described methodology or state that the code is publicly available. It only links to an extended version of the paper on arXiv. |
| Open Datasets | Yes | We evaluate our algorithm on five benchmark datasets, namely, EMNIST [Cohen et al., 2017], CIFAR10, CIFAR100 [Krizhevsky et al., 2009], CINIC10 [Darlow et al., 2018] and Tiny-Image Net (TINY) [Chrabaszcz et al., 2017]. |
| Dataset Splits | No | The paper describes data partitioning for client heterogeneity (e.g., s% of data from dominant classes, Dirichlet distribution) and mentions evaluation metrics, but it does not explicitly describe a validation dataset split (e.g., percentages or counts) used for hyperparameter tuning or model selection during training for its own method. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., specific GPU or CPU models, memory details). |
| Software Dependencies | No | The paper mentions using specific models (e.g., CNN, ResNet-8) and optimizers (mini-batch SGD), but it does not provide version numbers for any software dependencies like programming languages, frameworks (e.g., PyTorch, TensorFlow), or libraries. |
| Experiment Setup | Yes | The number of local training epochs is set to E = 1 and the number of global communication rounds is set to 100. We employ the mini-batch SGD as a local optimizer for all approaches. The batch size for each client is set as 20 and the learning rate η is set as 0.01. |