End to end machine learning pipeline
Data from a variety of sources like user data from a website or sensor data from a machine is collected and stored.
Machine learning model development
• To move data from the source (edge devices) to the central server (the machine where training happens), significant network bandwidth may be required which makes frequent training of the model tedious as data has to be moved whenever training is to be performed
• Data from a variety of devices or users are available at a single location which may raise some privacy and security concerns.
Federated Machine learning
Machine learning model deployment on edge devices
Training of the machine learning model on edge devices
Advantages of Federated Machine Learning
1. From a big data perspective, federated learning is much more scalable than the traditional pipeline as it eliminates the need for reliable network bandwidth and advanced hardware
2. The most important highlight of this approach is the privacy and security it provides. Since only the trained model from individual devices are sent out, private user information remains safe on the edge devices
3. Less data communication bandwidth requirement