Federated Learning Platform

Problem statement

Traditional machine learning relies on centralizing data for model training, posing
privacy risks. The challenge is to develop a Federated Learning Platform, allowing
devices to collaborate on model training without sharing sensitive raw data centrally.

Abstract

The project aims to create a Federated Learning Platform, revolutionizing the
conventional model training process. Federated Learning ensures that devices
collaborate in the model training process without sharing raw data centrally. The
platform emphasizes privacy preservation, as it allows local training on individual
devices while aggregating the collective knowledge to improve the overall model. This
approach opens doors for applications in healthcare, finance, and other sensitive
domains where data privacy is paramount.

Outcome

Create a smart learning system where devices collaborate on training machine
learning models without directly sharing private data. This ensures data safety,
fosters teamwork, and improves model predictions or decisions.

Reference

Federated learning (FL) is an approach that enables collaborative machine learning (ML) without sharing data over the network. Internet of Things (IoT) and Industry 4.0 are promising areas for FL adoption. Nevertheless, there are several challenges to overcome before the deployment of FL methods in existing large-scale IoT environments. In this paper, we present one step further toward the adoption of FL systems for IoT. More specifically, we developed a prototype that enables distributed ML model deployment, federated task orchestration, and monitoring of system state and model performance. We tested the prototype on a network that contains multiple Raspberry Pi for a use case of modeling the states of conveyors in an airport.

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   https://ieeexplore.ieee.org/document/9912211