This repository contains METU-FL team implementations for the Federated Tumor Segmentation Challenge 2021.
You can reach our related work here: https://link.springer.com/chapter/10.1007/978-3-031-09002-8_36
The list of given and implemented functions for Task 1 is listed below.
Aggregation function determines how the server side merges the collaborator model updates.
| Aggregation Function Name | Source |
|---|---|
| weighted_average_aggregation | Given by FeTS initiative |
| fedNova_simplified | METU-FL |
| make_aggregation_with_improved_nodes | METU-FL |
| FedAvgM | METU-FL |
| coordinatewise_median_aggregation | METU-FL |
Hyperparameter choice function determines the parameters for each FL round.
| Function Name | Source |
|---|---|
| constant_hyper_parameters | Given by FeTS initiative |
| lrscheduling_hyper_parameters | METU-FL |
| adaptive_epoch | METU-FL |
| adaptive_epoch_with_lr_scheduling | METU-FL |
Collaborator choice function determines which collaborators are chosen to train in each FL round.
| Function Name | Source |
|---|---|
| all_collaborators_train | Given by FeTS initiative |
| choose_random_nodes_with_faster_ones | METU-FL |
| random_collaborators_train | METU-FL |
| random_single_collaborator_train | METU-FL |
Validation metrics show the metrics to be computed each FL round.
| Function Name | Source |
|---|---|
| channel_sensitivity | Given by FeTS initiative |
| sensitivity | Given by FeTS initiative |
| specificity | Given by FeTS initiative |
- Hyperparameter choice function: adaptive_epoch_with_lr_scheduling
adaptive_epoch_with_lr_scheduling is an adaption of AdaComm with a learning scheduling scheme. The number of epochs per round decays according to the decrease in initial loss, and the learning rate decays according to the performance metric (average DICE score).
@inproceedings{MLSYS2019_c8ffe9a5,
author = {Wang, Jianyu and Joshi, Gauri},
booktitle = {Proceedings of Machine Learning and Systems},
editor = {A. Talwalkar and V. Smith and M. Zaharia},
pages = {212--229},
title = {Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD},
url = {https://proceedings.mlsys.org/paper/2019/file/c8ffe9a587b126f152ed3d89a146b445-Paper.pdf},
volume = {1},
year = {2019}
}
-
Collaborator choice function: all_collaborators_train
All collaborators participate in each FL round. -
Aggregation function: fedAvgM
Federated Averaging with server momentum uses accumulated gradients for the weight update.
@article{hsu2019measuring,
title={Measuring the effects of non-identical data distribution for federated visual classification},
author={Hsu, Tzu-Ming Harry and Qi, Hang and Brown, Matthew},
journal={arXiv preprint arXiv:1909.06335},
year={2019}
}
Validation metrics: We used the given default validation metrics but considering limited computational resources, we did not include Hausdorff Distance (include_validation_with_hausdorff = False).