j-hossain/federated-learning-blockchain-reference

Implementation of Federated Learning and Blockchain for training machine learning models using a decentralized approach thereby attempting to protect users' sensitive data .

Federated Learning with Blockchain

A simple application that uses Blockchain to demonstrate federated learning.

What is Federated Learning?

Machine learning models trained on sensitive real-world data promise improvements to everything from medical screening to disease outbreak discovery. And the widespread use of mobile devices means even richer and more sensitive data is becoming available. However, traditional machine learning involves a data pipeline that uses a central server(on-premise or cloud) that hosts the trained model to make predictions. Distributed Machine Learning (FL) in contrast, is an approach that downloads the current model and computes an updated model at the device itself (also known as edge computing) using local data. Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider) while keeping the training data decentralized. Most of the previous research-work in federated learning focuses on the transfer and aggregation of the gradients for learning on linear models and very less work is available on non-linear models. In this project, we explore a secure decentralized learning model using neural networks. The motivation for the same came from blockchain, preserving the user identities without a trusted central server and the hidden layers in a neural network, able to add non-linearity to the model. Analogous to the transfer of gradients in the federated learning system which requires a lot of network communication, we explore the possibility of broadcasting weights to the blockchain system. The goals of this work are to highlight research problems that are of significant theoretical and practical interest and to encourage research on problems that could have significant real-world impact.

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Setup

Make sure you have python3, pip setup on your machine.

Distributed Linear Regression Model

Working Code Dir: Federated(linear Regression)

Install librabries

• pygad
• numpy
• pandas
• pickle

Running of the Code

• Open 3 terminals
• In first terminal, run python server.py
• In second terminal, run python client1.py
• In third terminal, run python client2.py

Distributed Blockchain Model

Working Code Dir: Federated(linear regression + blockchain)

Structure of the block

Block -

• index
• client_model
• server_model
• cli - [“cli1”, “cli2”]
• timestamp
• previous_hash
• nonce

Hashing of the Block

Hash -

• index
• client_weights
• client_biases
• cli
• timestamp
• previous_hash
• nonce

Running of the Code

• Open 3 terminals
• In first terminal, run python server.py
• In second terminal, run python client1.py
• In third terminal, run python client2.py