Into to Federated Learning

Gusti Triandi Winata — Mon, 25 Nov 2024 09:09:12 +0000

Day 1 of 30 Days of Federated Learning Code.

OpenMined currently handling events that called curious learner to learn about Federated Learning from 20 November to 20 December and here I am also as participants that wanted to get into the learning journey as well.

The comic from google about Federated Learning shows a really insightful terminology and necessity of Federated Learning in Machine Learning systems regarding the privacy on the data side.

Data Privacy defines how a particular piece of information/data should be handled or who has authorized access based on its relative importance. With the introduction to AI (Machine Learning and Deep Learning), a lot of personal information can be extracted from these models, which can cause irreparable damage to the people whose personal data has been exposed. So, here comes the need to preserve this data while implementing various machine learning models.

Federated Learning which is also known as collaborative learning is a deep learning technique where the training takes place across multiple decentralized edge devices (clients) or servers on their personal data, without sharing it with other clients, thus keeping the data private. It aims at training a machine learning algorithm, say, deep neural networks on multiple devices (clients) having local datasets without explicitly exchanging the data samples.

Federated learning makes it possible for AI algorithms to gain experience from a vast range of data located at different sites. This approach enables several organizations to collaborate on the development of models but without having to share sensitive data with each other.

There are two types of Federated Learning:

Centralized federated learning: In this setting, a central server is used to orchestrate the different steps of algorithms and coordinate all the participating nodes during the learning process. The server is responsible for the nodes selection at the beginning of the training process and for the aggregation of the received model updates (weights). It is the bottleneck of the system.
Decentralized federated learning: In this type, nodes are able to coordinate themselves to obtain the global model. This setting prevents single point failures as the model updates are exchanged only between interconnected nodes.

As the learning commitment goes, here I put a daily blogs in this profile and I hope anyone find this learning logs to be informative and useful.

References

Your Getting Started Template Repository for Continuous and Automated Machine Learning Project with GitHub Actions

Gusti Triandi Winata — Thu, 09 Dec 2021 04:23:59 +0000

A starting template for continuous ML projects where data kept growing, models kept learning and services deployed continuously.

You can use this template for:

Initiate your ML projects
Set .dvc to track data & save models
Set cml to automate training and track experiment by pull requests
Set model serving to endpoint and frontend example then deploy your models continuously

My Workflow

Motivation

! Mention:

model stuck in research
model didn't get to production, repetitive task on modelling
machine assignment for training, mention cheaphead machine that burns when using their own machine for training
experiment tracking for reliability
data and models that grows time to time that needs its own version control

Had to have a reliable getting started template for machine learning project since most of project that had data version control, experiment management, deployment as a service, serving to sample frontend and collaboration with a yaml hyperparameter tuning file to ease experiment tracking.

Deliver actions diagrams, how it works specifically and how it would help with automating the project on specific events on repository.

Using github script, definition, and how it compares to another framework

Submission Category:

DIY Deployments/Wacky Wildcards

Yaml File or Link to Code

sanggusti / cml-streamlit-template

A Template Repository for Continuous Machine Learning Project with GitHub Actions and display it live with deployment on Streamlit

cml-streamlit-template

A Template Repository for Continuous Machine Learning Project with GitHub Actions and display it live with deployment on Streamlit

View on GitHub

Additional Resources / Info

[Note:] # (Be sure to link to any open source projects that are using your workflow!)

[Reminder]: # (Submissions are due on December 8th, 2021 (11:59 PM PT or 2 AM ET/6 AM UTC on December 9th).

DEV Community: Gusti Triandi Winata

Into to Federated Learning

References

Your Getting Started Template Repository for Continuous and Automated Machine Learning Project with GitHub Actions

My Workflow

Motivation

Submission Category:

Yaml File or Link to Code

sanggusti / cml-streamlit-template

A Template Repository for Continuous Machine Learning Project with GitHub Actions and display it live with deployment on Streamlit

cml-streamlit-template

Additional Resources / Info