Table of Contents
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<use case 2:Federated Learning in Data Gathering>
Deep learning is a promising way in aiding medical treatment and diagnosis, but an effective model requires quite large amounts of different data to converge. And the lack of sufficient training data is the biggest obstacle to developing AI models for medical applications.
Federated learning provides a safe tunnel across different medical institutions like hospitals and clinics, so that they can use train their own AI model with the data from multi-sources and without worry about the law issues and privacy problem with the help of Federated learning.
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Many cloud native monitoring applications are used to collect container/node resource usage and running log, like prometheus, elasticsearch.
The below image shows the overall architecture for using IEC as edge infrastructure in AI Edge.
Platform Architecture
The detailed platform architecture of AI Edge blueprint is shown in the below diagram.
In the current release the components released are
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The below image shows the software architecture for this release.
OTE-Stack features a pluggable architecture, making it much easier to build on.
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Function Perspective
Introduction to Eggroll. What is EggRoll?
Basic Architecture
Data flow between and within devices.
APIs
Federated ML application at edge R4 API Document
Hardware and Software Management
Software Management: Gerrit Repo
Licensing
Apache 2.0 license