How Critical is it for a Data Scientist to Adapt Federated Machine Learning?

Google introduced the term Federated Learning in 2016 to mark the beginning of a new machine learning approach in the ML paradigm. Federated learning resolves many shortcomings of the centralized and distributed training approaches. Without the use of federated learning, we would not have seen the highly improved on-device machine learning model like "Hey Google" in Google Assistant. To understand federated learning and its importance in today's IoT world, let me first describe the shortcomings of the existing models. The notion of machine learning started with centralized training.