# ki-ljl/FedProx-PyTorch

<https://repo.masters-masters.com/en/repos/ki-ljl/FedProx-PyTorch>

[Home](https://repo.masters-masters.com/en) › [ki-ljl](https://repo.masters-masters.com/en/owners/ki-ljl) › [FedProx-PyTorch](https://repo.masters-masters.com/en/repos/ki-ljl/FedProx-PyTorch)

ki-ljl/FedProx-PyTorch has 114 stars on GitHub. Its RepoMasters card: Prototype tier. PyTorch implementation of FedProx (Federated Optimization for…

| Field | Value |
| --- | --- |
| Owner | [ki-ljl](https://repo.masters-masters.com/en/owners/ki-ljl) |
| Stars | 114 |
| Forks | 20 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | MIT |
| Created | 2022 |
| Topics | federated-learning, fedprox, pytorch |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [ki-ljl/FedProx-PyTorch](https://github.com/ki-ljl/FedProx-PyTorch) |

## Repository

PyTorch implementation of FedProx (Federated Optimization for Heterogeneous Networks, MLSys 2020).

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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