Federated-Learning | paper Communication-Efficient Learning | Runtime Evironment library

by roxanneluo Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Federated-Learning Summary

Federated-Learning is a Python library typically used in Server, Runtime Evironment, Nodejs applications. Federated-Learning has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

We implement the paper Communication-Efficient Learning of Deep Networks from Decentralized Data. Its blog is here.

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Federated-Learning has a low active ecosystem.

It has 143 star(s) with 68 fork(s). There are 13 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 643 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Federated-Learning is current.

Quality

Federated-Learning has 0 bugs and 0 code smells.

Security

Federated-Learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Federated-Learning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Federated-Learning does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Federated-Learning releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Federated-Learning saves you 325 person hours of effort in developing the same functionality from scratch.

It has 780 lines of code, 65 functions and 9 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Register handlers
Start the next round
Stop the optimizer
Calculates the aggregate loss and accuracy for each client
Aggregate train loss and agr accuracies
Summarize the validation loss
Returns a dictionary of the training loss stats
Convert a Python object to a pickle string
Convert a string to a Python object
Generate dummy data
Generate dummy weights
Apply pre - processing
Sample a single non - id
Train the model
Start the server

Get all kandi verified functions for this library.

Federated-Learning Key Features

No Key Features are available at this moment for Federated-Learning.

Federated-Learning Examples and Code Snippets

No Code Snippets are available at this moment for Federated-Learning.

Community Discussions

Trending Discussions on Federated-Learning

Accuracy decreasing after iteration in federated learning setting

Pysyft Federated learning, Error with Websockets

QUESTION

Accuracy decreasing after iteration in federated learning setting

Asked 2021-Mar-31 at 12:37

I am working on a federated learning to detect bad clients.

Brief about federated learning - Data is divided into various clients, training is done on client side and the results are then sent by each client to central server where aggregation of the client weights is done and the aggregated model is then again sent to local clients for training.

I am working on detection of client sending malicious updates to central server. I am using base code present here.

I wrote a method filter client which will detect if some client is malicious and remove that client from aggregation step. I expected that there will not be much performance difference if one of the client weight is remove from global aggregation but the results are confusing me. I added this piece of code. noisy_client[itr] != 0 will only occur for 1/10 clients and it will occur for the same client in each iteration.

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ANSWER

Answered 2021-Mar-31 at 12:37

A common problem could be that you are trying to aggregate in a no_grad() scope. Happened to me once. The optimizer was essentially resetting once every federated round even though the models are being aggregated.

This is a hunch as I can't say more since I haven't seen any code.

Source https://stackoverflow.com/questions/66576677

QUESTION

Pysyft Federated learning, Error with Websockets

Asked 2020-Feb-19 at 02:38

I am trying to run a federated learning from pysyft (https://github.com/OpenMined/PySyft/blob/dev/examples/tutorials/advanced/websockets-example-MNIST-parallel/Asynchronous-federated-learning-on-MNIST.ipynb) that creates remote workers and connect to them via websockets. however I am getting an error in folllowing evaluation step.

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ANSWER

Answered 2020-Feb-19 at 02:38

I came across this problem as well and pushed a fix in https://github.com/OpenMined/PySyft/pull/2948

Source https://stackoverflow.com/questions/60202610

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

Vulnerabilities

No vulnerabilities reported

Install Federated-Learning

You can download it from GitHub.
You can use 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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