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.

Hyperparameter choice function determines the parameters for each FL round.

Collaborator choice function determines which collaborators are chosen to train in each FL round.

Validation metrics show the metrics to be computed each FL round.

• 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).