Personalized Federated Learning With a Graph
Authors: Fengwen Chen, Guodong Long, Zonghan Wu, Tianyi Zhou, Jing Jiang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on traffic and image benchmark datasets can demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney 2University of Washington, Seattle 3University of Maryland, College Park |
| Pseudocode | Yes | Algorithm 1 Structural Federated Learning Server. |
| Open Source Code | Yes | All implementation codes are available on Github8. https://github.com/dawenzi098/SFL-Structural-Federated Learning |
| Open Datasets | Yes | We used four traffic datasets, METR-LA, PEMSBAY, PEMS-D4, and PEMS-D8 to observe the performance of the SFL in different real-world scenarios. We apply the same data pre-processing procedures as described in [Wu et al., 2019]. For the image datasets, we applied the same train/test splits as in the work 9. We artificially partitioned the CIFAR-10 with parameter k(shards) to control the level of non-IID data. |
| Dataset Splits | Yes | We also apply Z-score normalization to the inputs and separate the training-set, validation-set, and test-set in a 70% 20% and 10% ratio. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions general software components like "pure RNN" and "Res Net9" but does not specify their version numbers or other ancillary software dependencies with versions required for replication. |
| Experiment Setup | Yes | We employ SGD with the same learning rate as the optimizer for all training operations, use 128 for batch size, and the number of total communication rounded to 20. |