Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On the Convergence of FedAvg on Non-IID Data
Authors: Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, Zhihua Zhang
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically verify our results through numerical experiments. |
| Researcher Affiliation | Academia | Xiang Li School of Mathematical Sciences Peking University Beijing, 100871, China EMAIL Kaixuan Huang School of Mathematical Sciences Peking University Beijing, 100871, China EMAIL Wenhao Yang Center for Data Science Peking University Beijing, 100871, China EMAIL Shusen Wang Department of Computer Science Stevens Institute of Technology Hoboken, NJ 07030, USA EMAIL Zhihua Zhang School of Mathematical Sciences Peking University Beijing, 100871, China EMAIL |
| Pseudocode | No | The paper describes the algorithm steps in a descriptive paragraph under 'Algorithm description' in Section 2, but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing its code or a link to a code repository. |
| Open Datasets | Yes | We distribute MNIST dataset (Le Cun et al., 1998) among N = 100 workers in a non-iid fashion such that each device contains samples of only two digits. |
| Dataset Splits | No | The paper describes how data is distributed across devices and how the model is evaluated during training, but it does not specify explicit training/validation/test dataset splits for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The regularization parameter is set to λ = 10 4. In each round, all selected devices run E steps of SGD in parallel. We decay the learning rate at the end of each round by the following scheme ηt = η0 1+t, where η0 is chosen from the set {1, 0.1, 0.01}. For unbalanced MNIST, we use batch size b = 64. The hyperparameters are the same for all schemes: E = 20, K = 10 and b = 64. |