Code for the Federated Learning project. Checkpoints of the performed experiments can be found at this link.
You can use CoLab to run the code.
The repository supports experiments on the following datasets:
- Reduced Federated IDDA from FedDrive [1]
- Task: semantic segmentation for autonomous driving
- 24 users
- Reduced GTA 5 datasets [2]
The main.py orchestrates training. All arguments need to be specified through the args parameter (options can be found in utils/args.py).
CoLab notebook examples can be found in the homonymous folder.
[1] Fantauzzo, Lidia, et al. "FedDrive: generalizing federated learning to semantic segmentation in autonomous driving." 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022.
[2] Richter, S.R., Vineet, V., Roth, S., Koltun, V. (2016). Playing for Data: Ground Truth from Computer Games. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science(), vol 9906. Springer, Cham. https://doi.org/10.1007/978-3-319-46475-6_7