c-gabri / Federated-Learning-PyTorch

PyTorch implementation of Federated Learning algorithms FedSGD, FedAvg, FedAvgM, FedIR, FedVC, FedProx and standard SGD, applied to visual classification. Client distributions are synthesized with arbitrary non-identicalness and imbalance (Dirichlet priors). Client systems can be arbitrarily heterogeneous. Several mobile-friendly models are provided

Date Created 2022-01-05 (3 years ago)

Commits 204 (last one 3 years ago)

Stargazers 77 (0 this week)

Watchers 2 (0 this week)

Forks 15

License gpl-3.0

Ranking

RepositoryStats indexes 635,689 repositories, of these c-gabri/Federated-Learning-PyTorch is ranked #368,112 (42nd percentile) for total stargazers, and #483,706 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #70,847/130,103.

c-gabri/Federated-Learning-PyTorch is also tagged with popular topics, for these it's ranked: pytorch (#4,171/6227), classification (#373/575), federated-learning (#119/223)

Other Information

c-gabri/Federated-Learning-PyTorch has 1 open pull request on Github, 2 pull requests have been merged over the lifetime of the repository.

Github issues are enabled, there are 2 open issues and 0 closed issues.

All Topics

sgd fedir fedvc fedavg fedsgd fedavgm fedprox non-iid pytorch imbalance heterogeneity classification federated-learning

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137 commits on the default branch (master) since jan '22

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updated: 2025-03-30 @ 10:11am, id: 444874361 / R_kgDOGoQ-eQ