Challenges in Adopting Federated Learning

Challenges in Adopting Federated Learning

To begin with this discussion, we can say that as a new field in Machine Learning, Federated Learning has to overcome many challenges. In this post on Challenges in Adopting Federated Learning, I will highlight the open issues and challenges that we still have to overcome.

Basically, Federated Learning is a new kind of Machine Learning technique that has the potential to solve many data privacy issues. However, there are challenges in its adoption in Production Systems. The following section provides a list of some of those challenges that are yet to be addressed.

Challenges in Adopting Federated Learning

Without a doubt we can say Federated Learning technique has potential to preserve the privacy of user data. However, there are certain challenges for implementing this technique in real systems which we need to address.

Loss of Connectivity

As Federated Learning suggests, the personal data of users should remain at local devices. Also, the partial machine learning models should be created there only. Later, these local devices can transfer this model to the aggregating server.

However, the training takes lots of time and during this time the connectivity may not be available. In order to overcome this challenge, Federated Learning should give priority to those participating devices which are static or currently not in use.

Heterogeneous Data

It is important to realize that the data that individual devices using for learning the machine learning model may be heterogeneous and consist of different types of features. Such unbalance in data causes the model to converge slowly and degrades the performance.

Security Concerns

In fact, a malicious user may alter the learned model when the transfer is taking place between a device and the aggregation server thereby corrupting the resulting model. Another security concern is that the fake nodes may participate in the learning process.

Communication Issues among Heterogeneous Nodes

In addition, communication among participating nodes which are heterogeneous in nature is also a challenge.

Large Number of Participating Nodes are Required

Basically, Federated Learning requires the participation of millions of nodes in the training process. In other words, bringing millions of devices to enable training of the model is a real challenge in the deployment of a Federated Learning model.

Summary

Today I discussed some of the Challenges in Adopting Federated Learning that need to be addressed for implementing systems using Federated Learning.


Related Topics

The Python Libraries that you should Learn for Analyzing IoT Data

How Internet-of-Things (IoT) is related to Data Science

Collaborating Model Training using Federated Learning

Federated Learning Architectures and Their Applications

Prominent Applications of Deep Learning in Agriculture

Privacy and Security Mechanisms for Federated Learning

Machine Learning