On the Role of Server Momentum in Federated Learning
Authors: Jianhui Sun, Xidong Wu, Heng Huang, Aidong Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate the effectiveness of our proposed framework. and 6 Experimental Results In this section, we present empirical evidence to verify our theoretical findings. We train Res Net (He et al. 2016) and VGG (Simonyan and Zisserman 2015) on CIFAR10 (Krizhevsky 2009). |
| Researcher Affiliation | Academia | 1Computer Science, University of Virginia, VA, USA 2Electrical and Computer Engineering, University of Pittsburgh, PA, USA 3Computer Science, University of Maryland College Park, MD, USA |
| Pseudocode | Yes | Algorithm 1: Fed OPT (Reddi et al. 2020): A Generic Formulation of Federated Optimization and Algorithm 3: Multistage Fed GM |
| Open Source Code | No | No explicit statement or link providing access to the open-source code for the methodology described in this paper. |
| Open Datasets | Yes | We train Res Net (He et al. 2016) and VGG (Simonyan and Zisserman 2015) on CIFAR10 (Krizhevsky 2009). |
| Dataset Splits | No | The paper does not explicitly mention train/validation/test dataset splits or cross-validation setup. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud instance types) are provided for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA) are listed in the paper. |
| Experiment Setup | Yes | Unless specified otherwise, we have 100 clients in all experiments, and the partial participation ratio is 0.05, i.e., 5 out of 100 clients are picked in each round, non-i.i.d. is α = 0.5, and local epoch is 3. and We perform grid search over η {0.5, 1.0, 1.5, . . . , 5.0}, β {0.7, 0.9, 0.95}, and ν {0.7, 0.9, 0.95}. |