Enzo Zhang  zifan wu  haihui wang

The data cross providers about same object combined the whole information for Federated Machine Learning in AI Edge case. Each side has no ability to hold all of  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 collect and exchange data between edges. Meanwhile a method must be applied to make sure the communication secure enough for all participants.

See AI edge application case 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 ML
• Data security

Application groups data from edges base security method, Data privacy is still going to 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 with required data was send back to edge for next process.

Many key components are required to make the case works well and machine learning run success.

1. Data transfer and group
2. Data collect and distribute
3. Federated Machine Learning framework
4. Security encryption algorithm

Functionality integrated with framework make edge application run in right way and keep data security.