A Federated Learning Framework which is Heterogeneous and Flexible.
Project description
FedHF
FedHF is a loosely coupled, Heterogeneous resource supported, and Flexible federated learning framework.
Accelerate your research
Features
- Losely coupled
- Heterogeneous resource supported
- Flexible federated learning framework
- Support for asynchronous aggregation
- Support for multiple federated learning algorithms
Algorithms Supported
Synchronous Aggregation
- [FedAvg] Communication-Efficient Learning of Deep Networks from Decentralized Data(AISTAT) [paper]
Asynchronous Aggregation
- [FedAsync] Asynchronous Federated Optimization(OPT2020) [paper]
Tiered Aggregation
- [TiFL] TiFL: A Tier-based Federated Learning System (HPDC 2020) [paper]
Getting Start
pip install fedhf
# If you want to use wandb to view log, please login first
wandb login
You can see the Document for more details.
Contributing
For more information, please see the Contributing page.
Citation
In progress
Licence
This work is provided under Apache License Version 2.0.
Acknowledgement
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
fedhf-0.3.0.tar.gz
(35.9 kB
view details)
Built Distribution
fedhf-0.3.0-py3-none-any.whl
(71.1 kB
view details)
File details
Details for the file fedhf-0.3.0.tar.gz
.
File metadata
- Download URL: fedhf-0.3.0.tar.gz
- Upload date:
- Size: 35.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b1f7abb0a6d70e94fe905b8eb924478aab4375b5191a4d38b3f49014fa5464e4 |
|
MD5 | b3941311b62a66f3b7590c50e8359c59 |
|
BLAKE2b-256 | 2401ebd6771e7fddc4ad5f8e34242bc50caa3be4f0e6855a4aee285c5482a840 |
File details
Details for the file fedhf-0.3.0-py3-none-any.whl
.
File metadata
- Download URL: fedhf-0.3.0-py3-none-any.whl
- Upload date:
- Size: 71.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 058fec79d8d04bc26939499de2ce5668769cacaeae18539b0316e6797e2cd2ca |
|
MD5 | 3328138134a0ca68c127e06e9c49a86b |
|
BLAKE2b-256 | c6080b675fb4e3f6a4c30afb170dac77de078b1264512ec93cdf9d61b83867bf |