Federated Learning is a technique of machine Learning that aims in preserving the privacy of user data. While in this process it enables the training of a Machine Learning (ML) model. Today I will discuss the applications of Federated Learning in IoT Applications. Since the sensor devices in IoT applications generate a massive amount of user data, it is … Read more
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 … Read more
In this post on Privacy and Security Mechanisms for Federated Learning, I will discuss some of the mechanisms for achieving the privacy in Federated Learning approach. Although, these issues are still the topic of research, yet few techniques are emerging that we can use in preserving the privacy of user data. Why Privacy Mechanisms are … Read more
In this post on Federated Learning Architectures and Their Applications, I will talk about various architectures for implementing Federated Learning and application scenarios of each of them. In my earlier post on Federated Learning, I have discussed the basic concepts and pros and cons of this new technique of building machine learning models. However, when we … Read more
In this post on Collaborating Model Training using Federated Learning, I will explain how the training of a neural network occurs without the need of collecting the whole training data on a centralized location. Basically, Federated Learning is a special form of Machine Learning, which requires the model training at local participants’ location and only … Read more
