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].
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
Authors: Yifan Yang, Peiyao Xiao, Kaiyi Ji
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the effectiveness of the proposed methods over existing FBO algorithms. In this section, we perform two hyper-representation experiments to compare the performance of our proposed Sim FBO algorithm with FBO-Agg ITD [69], Fed Nest [69], and LFed Nest [65], and validate the better performance of Shro FBO in the presence of heterogeneous local computation. We test the performance on MNIST and CIFAR datasets with MLP and CNN backbones. |
| Researcher Affiliation | Academia | Yifan Yang, Peiyao Xiao and Kaiyi Ji Department of Computer Science and Engineering University at Buffalo Buffalo, NY 14260 EMAIL |
| Pseudocode | Yes | Algorithm 1 Sim FBO and Shro FBO |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We test the performance on MNIST and CIFAR datasets with MLP and CNN backbones. The left and middle ones plot the training accuracy v.s. # of communication rounds on i.i.d. MNIST datasets with MLP networks, and the right one plots the training accuracy v.s. # of rounds on i.i.d. CIFAR-10 datasets with a 7-layer CNN. |
| Dataset Splits | No | The paper mentions using MNIST and CIFAR datasets and following the experimental setup from [65, 69], but it does not explicitly provide specific training/validation/test split percentages or sample counts in the provided text. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions using MLP and CNN networks but does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper states, 'The details of all experimental specifications can be found in Appendix A.1.', but these details, including specific hyperparameters or training settings, are not present in the provided main text. |