MetisFL
FedScale
| MetisFL | FedScale | |
|---|---|---|
| 43 | 4 | |
| 531 | 367 | |
| 0.0% | 1.6% | |
| 9.3 | 7.9 | |
| 6 months ago | 5 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MetisFL
- [D] Scaling Neuroscience Research Using Federated Learning
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We created an open source SDK. Any feedback is important for us!
We have created a new project on GitHub called MetisFL, a federated learning framework that allows developers to federate their machine learning workflows and train their models across distributed datasets without having to collect the data in a centralized location.
- Show HN: New GitHub Project Help Needed
- A lighting-fast and developer-friendly Federated Learning SDK
- Looking for open source developers on GitHub
- Enterprise-ready and developer-friendly Federated Learning SDK
FedScale
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University of Michigan Researchers Open-Source ‘FedScale’: a Federated Learning (FL) Benchmarking Suite with Realistic Datasets and a Scalable Runtime to Enable Reproducible FL Research on Privacy-Preserving Machine Learning
Continue reading | Checkout the paper, github link
- We created the most comprehensive benchmark datasets for federated learning to date!
- The most comprehensive benchmark datasets for federated learning to date!
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The most comprehensive benchmark datasets for federated learning to date
We created FedScale, which has a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research. FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, word prediction, and speech recognition. Our evaluation platform provides flexible APIs to implement new FL algorithms and includes new execution backends with minimal developer efforts. Check it out, and feel free to join the FedScale community via Slack(https://join.slack.com/t/fedscale/shared_invite/zt-uzouv5wh-ON8ONCGIzwjXwMYDC2fiKw)!
Paper: https://arxiv.org/abs/2105.11367 and Github repo: https://github.com/symbioticlab/fedscale
Cheers!
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