The traditional way of using integrated tools for data mining and research analysis is no longer practical since the data is too large to manage. In recent times, distributed and federated ML are being favoured approaches as they allow for larger data analysis. While the two concepts appear similar, there is a considerable difference between the two. In this concept we explore how these two approaches are different from each other.

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