The classical machine learning paradigm requires the aggregation of user data in a central location where data scientists can pre-process it, calculate features, tune models and evaluate performance. The advantages of this approach include being able to leverage high-performance hardware (such as GPUs), and the scope for a data science team to perform in-depth data analysis to improve model performance.
However, this data centralization comes at a cost to data privacy and may also fall foul of data sovereignty laws. Also, centralized training…
