An introduction to federated machine learning

Let us first look at what federated learning is and why it is a valuable tool. We will first look at privacy challenges that are faced while applying machine learning, followed by how and why we apply federated learning.

Privacy challenges in machine learning

Traditional ML involves a series of steps that we have discussed multiple times so far: data preprocessing, feature extraction, model training, and tuning the model for best performance. However, this involves the data being exposed to the model and, therefore, is based on the premise of the availability of data. The more data we have available, the more accurate the model will be.

However, there is often a scarcity of data in the real world. Labels are hard to come by, and there is no centrally aggregated data source. Rather, data is collected and processed by multiple entities who may not want to share it.

This is true more often than not in the security space. Because...