Practicing-Federated-Learning
kandi X-RAY | Practicing-Federated-Learning Summary
kandi X-RAY | Practicing-Federated-Learning Summary
Practicing-Federated-Learning is a Python library. Practicing-Federated-Learning has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Practicing-Federated-Learning build file is not available. You can download it from GitHub.
Practicing-Federated-Learning
Practicing-Federated-Learning
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Quality
Security
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Practicing-Federated-Learning has a low active ecosystem.
It has 476 star(s) with 197 fork(s). There are 31 watchers for this library.
It had no major release in the last 6 months.
There are 14 open issues and 3 have been closed. On average issues are closed in 3 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Practicing-Federated-Learning is current.
Quality
Practicing-Federated-Learning has 0 bugs and 0 code smells.
Security
Practicing-Federated-Learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Practicing-Federated-Learning code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Practicing-Federated-Learning is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
Practicing-Federated-Learning releases are not available. You will need to build from source code and install.
Practicing-Federated-Learning has no build file. You will be need to create the build yourself to build the component from source.
Top functions reviewed by kandi - BETA
kandi has reviewed Practicing-Federated-Learning and discovered the below as its top functions. This is intended to give you an instant insight into Practicing-Federated-Learning implemented functionality, and help decide if they suit your requirements.
- Register handlers
- Calculates the aggregate loss map for each client
- Aggregate the loss map for all clients
- Calculates the accuracy of the training loss
- Register socket handles
- Return the load averages
- Train one round of the model
- Performs the forward computation
- Build the target tensors
- Generates a VOC2 JSON file for VOC2
- Forward the image
- Crop a bounding box
- Performs local training
- Encrypt a number
- Train the local model
- Test the roi module
- Evaluate the model
- Generate a paillier key pair
- Encrypt a value
- Reads thebreast dataset
- Train one epoch
- Decompose VGG16
- Creates a PaillierPrivateKey from a totient
- Returns a Dataset object
- Decrypt the encrypted number
- Forward computation
- Convert an annotation file
Get all kandi verified functions for this library.
Practicing-Federated-Learning Key Features
No Key Features are available at this moment for Practicing-Federated-Learning.
Practicing-Federated-Learning Examples and Code Snippets
No Code Snippets are available at this moment for Practicing-Federated-Learning.
Community Discussions
No Community Discussions are available at this moment for Practicing-Federated-Learning.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Practicing-Federated-Learning
You can download it from GitHub.
You can use Practicing-Federated-Learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use Practicing-Federated-Learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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