# diaoenmao/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients

<https://repo.masters-masters.com/en/repos/diaoenmao/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients>

[Home](https://repo.masters-masters.com/en) › [diaoenmao](https://repo.masters-masters.com/en/owners/diaoenmao) › [HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients](https://repo.masters-masters.com/en/repos/diaoenmao/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients)

diaoenmao/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients has 183 stars on GitHub. Its RepoMasters card…

| Field | Value |
| --- | --- |
| Owner | [diaoenmao](https://repo.masters-masters.com/en/owners/diaoenmao) |
| Stars | 183 |
| Forks | 41 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | MIT |
| Created | 2019 |
| Topics | deep-learning, federated-learning, model-compression, distributed-machine-learning, efficient-machine-learning |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [diaoenmao/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients](https://github.com/diaoenmao/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients) |

## Repository

\[ICLR 2021\] HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

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

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