Federated Prompt Learning for Weather Foundation Models on Devices
Authors: Shengchao Chen, Guodong Long, Tao Shen, Jing Jiang, Chengqi Zhang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrates Fed Po D leads the performance among state-of-the-art baselines across various setting in realworld on-device weather forecasting datasets. |
| Researcher Affiliation | Academia | Shengchao Chen , Guodong Long , Tao Shen , Jing Jiang and Chengqi Zhang Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney shengchao.chen.uts@gmail.com, {guodong.long, tao.shen, jing.jiang, chengqi.zhang}@uts.edu.au |
| Pseudocode | Yes | Algorithm 1 Implementation of PT and PV Updating, Algorithm 2 Implementation of Fed Po D |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the Fed Po D methodology or explicitly state that the code is released. |
| Open Datasets | Yes | Three weather multivariate time-series datasets from [Chen et al., 2023b], including Ave PRE, Sur TEMP, and Sur UPS collected by 88, 525, and 238 devices, respectively. Detailed information can be found at Appendix A. |
| Dataset Splits | No | The paper does not explicitly state train/validation/test dataset splits by percentages or sample counts. It mentions training over epochs and communication rounds, but not the data partitioning strategy for validation. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | Main experiments are conducted in 25 local epoch within 50 communication round. Following [Chen et al., 2022], Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used as evaluation metrics. All results are in 100 the original value for a clearer comparison. Detailed information about the implementation, local updating process and the aggregation can be found at Appendix B. Our configuration is as follows: 5 local epochs and 10 communication rounds, while other settings follow main experiments. |