Comments (2)
stheertha
commented on May 18, 2024
1
Thanks for being interested in contributing to fedjax. We are in the process of setting up the framework to accept external contributions. Will let you know as soon as we have the necessary pieces in place.
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jaehunro
commented on May 18, 2024
1
Thanks for your patience.
We are now ready to accept community contributions. We recommend reading through the contributing guidelines as well as the developer workflow.
Add FedPA to fedjax.algorithms. Please refer to fed_avg.py and fed_avg_test.py for an example internal implementation and unit tests for FedAvg. Additionally, as mentioned in the contributing guidelines, please also add an associated binary that runs FedPA on EMNIST that reproduces the paper results.
Please reach out if you have any additional questions.
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Related Issues (17)
- Clarifying the meaning of "weight" HOT 1
- Centralized (server-only) algorithm HOT 1
- Implementing SCAFFOLD HOT 2
- Feature request: Convert standard dataset into a federated dataset HOT 5
- PatentMime^
- Full EMNIST example does not exhibit parallelization HOT 2
- FedJax depends on TensorFlow Federated? HOT 7
- How to create a validation dataset? HOT 4
- Problem of Quick Start in Readme.md HOT 2
- Support for haiku models with non-trainable state HOT 2
- Support for gldv2 and inaturalist datasets HOT 7
- Support for manually modifying client/server learning rate HOT 1
- CIFAR 100 Questions HOT 5
- Implement standard CIFAR-100 model in fedjax.models.cifar100 HOT 1
- Add support for stateful clients HOT 8
- Error of the Stackoverflow Tokernizer example HOT 1
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