https://www.lfedge.org/2021/10/21/akraino-r5-spotlight-on-the-ai-edge-federated-machine-learning-at-the-edge/

Chaoyu Zhang rolandwu haihui wang Paula Black

By: Enzo Zhang Zifan Wu Haihui Wang, and Ryan Anderson

All attributes of each row data for Federated Machine Learning (ML) come from more than one edge provider. Each side has no ability on its own to hold all of the required data to function as designed. Specific edges (or edge sides) can be pretty limited in terms of edge computing capabilities for a variety of reasons/conditions

The application must cover more than one edge while also collecting and exchanging data between edges. Meanwhile, a method must be applied to make sure the communication between these edges is secure enough for all participants.

Here are some examples of AI edge application use cases to illustrate:

Deploy more than one edge
No more extra storage space
A few computing units
Deal with data locally
Network ability
Share data each other
Work together with ML
Data security

Applications group data from edges based on security methods; but data privacy is still managed by the Federated Machine Learning framework. Data, as an input of a ML model, is in a central site, and exchange of data occurs after ML processes are done. Required data gets sent back to the edge for its next process.

Many key components are required to make the case work well and machine learning run successfully:.

Data transfer and group
Data collect and distribute
Federated Machine Learning framework
Security encryption algorithm

This functionality, integrated with the framework, can enable edge applications to run right correctly and securely.

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