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All attributes of each row data for Federated Machine Learning come from more than one data providersedge provider. They must be combined before the whole information for Federated Machine Learning as input of Federated ML model in AI Edge case. Each side has no ability to hold all of all required data required. Edge side has much pretty limitation for edge computing because of variety of conditions.
The application must cover edges more than one and to collect and exchange data between edges. Meanwhile a method must be applied to make sure the communication secure enough for all participants to protect data.
See Here are some AI edge application case characters as following.
- Deploy more than one edge
- No more extra storage space
- A few computing units
- Deal with data locally
- Network ability
- Share data each others
- Work together with MLFML framework
- Data security
Application groups data from edges base on security encryption method, . Data privacy is still going to be managed by Federated Machine Learning framework. Data as input of machine learning model was in a central site, data exchange was placed after machine learning done. Model hold in all edges after exchange in secure way, then learning process execute. Federated Machine Learning model with required data was send back to used in edge side for next process.
Many key components are required to make the case works well and machine learning run success.
- Data transfer transfering and groupgrouping
- Data collect and distribute
- Federated Machine Learning framework
- Security Secure encryption algorithm
Functionality integrated with framework make edge application run in right way and keep data securityFederated Machine Learning framework support AI Edge application to run in transparent way with high functionality.