Federated Learning

Federated learning is a type of machine learning in which the process in executed in the user devices instead sending the user data to the server.

As we know how google takes user data which increase the privacy issues but we know that they take data for making our user experience better through machine learning that’s why they take our data which they store in their data center. To eliminate the sending the data packet to the server. Google introduced a new concept called federated learning.

Federated learning enables multiple actors to build a common. Robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security, data access right and access to heterogeneous data.

How the federated learning works?

There are mainly three steps:

1.      Request.

2.      Response.

3.      Report.

1.Firstly, the device sends a request to the server for the related data required for the process to be conducted.

2.Server responds to the request and send the confirmation data and the algorithm which is required to process the user data into report.

3.Training part is done on the devices and after the training is completed report is send to the server which is then use to machine learning.

But still federated learning is not perfect it has many problem or challenges.

1. One of the challenges is communication bandwidth. Federated learning on mobile phones relies on wireless communication to collaboratively learn a machine learning model. Although compute resources of mobile phones are becoming increasingly powerful, the bandwidth of wireless communication has not increased as much. As such, the bottleneck is shifted from computation to communication. As a consequence, limited communication bandwidth could incur long communication latency, and thus could significantly slow down the convergence time of the federated learning process.

2. Another challenge that federated learning needs to address is the reliability of end devices which participate in the federated learning process. Federated learning is an iterative process, it relies on the participating end devices to continuously communicate over iterations until the learning process converges. However, in real-world deployments, due to various practical reasons, not all end devices may fully participate in the complete iterative process from beginning to end.