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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Personalized Federated Learning With a Graph
Authors: Fengwen Chen, Guodong Long, Zonghan Wu, Tianyi Zhou, Jing Jiang
IJCAI 2022 | Venue PDF | 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. |