FedShapley | Profit Allocation for Federated Learning | Dataset library

by BUAA-BDA Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | FedShapley Summary

FedShapley is a Python library typically used in Artificial Intelligence, Dataset, Tensorflow applications. FedShapley has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However FedShapley build file is not available. You can download it from GitHub.

MNIST data set

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Quality

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FedShapley has a low active ecosystem.

It has 6 star(s) with 4 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

FedShapley has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FedShapley is current.

Quality

FedShapley has no bugs reported.

Security

FedShapley has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

FedShapley is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

FedShapley releases are not available. You will need to build from source code and install.

FedShapley 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 FedShapley and discovered the below as its top functions. This is intended to give you an instant insight into FedShapley implemented functionality, and help decide if they suit your requirements.

Perform the model validation
Read test images from file
Read test labels from file
Train a group of agents
Perform federated train
Dump data to file
Get the data for a digit combination
This function parses the MR txt file
Normalize the input list
Get data for federated agents
Check if x is a range
Appends a list of features to a file
Prepare dataset
Appends a set of mutations to a file
Determine the performance of a given agent set
Local training function
Return a list of fermi files
Get the data for a given digit
Removes the removed item from the list
Power sets of a binary sequence
Returns a list of Permutation objects
Write the cached performance data to a file
Loads gradients from an ini file
Solves a finite factor
Get the weights for a given agent
Train the model with the given parameters

Get all kandi verified functions for this library.

FedShapley Key Features

No Key Features are available at this moment for FedShapley.

FedShapley Examples and Code Snippets

No Code Snippets are available at this moment for FedShapley.

Community Discussions

Trending Discussions on FedShapley

'tensorflow_federated' has no attribute 'NamedTupleType

QUESTION

'tensorflow_federated' has no attribute 'NamedTupleType

Asked 2020-Dec-28 at 03:37

I am following this code https://github.com/BUAA-BDA/FedShapley/tree/master/TensorflowFL and trying to run the file same_OR.py

I also place input file "initial_model_parameters.txt" and data folder "MNIST_data" in same folder

...

ANSWER

Answered 2020-Dec-28 at 03:37

tff.NamedTupleType was renamed to tff.StructType in TFF version 0.16.0 (release notes).

Two options:

Install a pre-0.16.0 version of TFF: this should be doable with pip install tensorflow_federated=0.15.0.
Update the code: the error should go away after replacing the tff.NamedTupleType with tff.StructType in the snippet:

Source https://stackoverflow.com/questions/65471612

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install FedShapley

You can download it from GitHub.
You can use FedShapley 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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