BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning

Last update: Nov 20, 2022

Overview

BEAS

Blockchain Enabled Asynchronous and Secure Federated Machine Learning

Default Network Configuration:

The default application uses the HyperLedger Test network which bootstraps the following instances:

1 Orderer
1 Certifying Authority
2 org (org0 and org1) maintaining 2 peer (peer0 and peer 1)
1 CouchDB
1 CLI

Usage Instructions:

Prerequisites:

HyperLedger Fabric v2.2.x LTS
Download this repository, and merge BEAS/fabric-samples with the HyperLedger fabric-samples directory.

Network Setup:

$  cd fabric-samples/BEAS
$  ./teardownBEAS.sh
$  ./startBEAS.sh

If ./sh files have permission error (mac OS):

$  chmod u+r+x ./file_name.sh

Running the Application:

Initialise New Channel

Change working directory:

$  cd fabric-samples/BEAS/javascript/storageServer

Install Application Dependancies:

$  npm install

Run Application

$  node server.js

Initialise New Client

Initialise New Channel

Change working directory:

$  cd fabric-samples/BEAS/javascript/clientNode

Install Application Dependancies:

$  npm install

Run Application

$  node client.js

To view the frontend, go to your browser and lauch http://localhost:5000

BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning

Related tags

Overview

BEAS

Default Network Configuration:

Usage Instructions:

Prerequisites:

Network Setup:

Running the Application:

Initialise New Channel

Initialise New Client

Initialise New Channel

Owner

Harpreet Virk

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BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning

Related tags

Overview

BEAS

Default Network Configuration:

Usage Instructions:

Prerequisites:

Network Setup:

Running the Application:

Initialise New Channel

Initialise New Client

Initialise New Channel

Owner

Harpreet Virk

SEC'21: Sparse Bitmap Compression for Memory-Efficient Training onthe Edge

GraphGT: Machine Learning Datasets for Graph Generation and Transformation

The repository for freeCodeCamp's YouTube course, Algorithmic Trading in Python

Learn the Deep Learning for Computer Vision in three steps: theory from base to SotA, code in PyTorch, and space-repetition with Anki

PyTorch code for the paper "FIERY: Future Instance Segmentation in Bird's-Eye view from Surround Monocular Cameras"

Towers of Babel: Combining Images, Language, and 3D Geometry for Learning Multimodal Vision. ICCV 2021.

The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction".

Create UIs for prototyping your machine learning model in 3 minutes

This is the code for the paper "Motion-Focused Contrastive Learning of Video Representations" (ICCV'21).

Systemic Evolutionary Chemical Space Exploration for Drug Discovery

Using PyTorch Perform intent classification using three different models to see which one is better for this task

JupyterNotebook - C/C++, Javascript, HTML, LaTex, Shell scripts in Jupyter Notebook Also run them on remote computer

Attendance Monitoring with Face Recognition using Python

This implements one of result networks from Large-scale evolution of image classifiers

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

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