Machine learning has revolutionized the way we analyze data and make predictions. However, traditional machine learning methods require large amounts of data to be centralized in one place for analysis, which can raise privacy concerns and create challenges for companies that don’t have access to such data.
Federated learning is a solution to these challenges. It’s a distributed machine learning approach that allows data to remain on local devices, while still enabling models to be trained on the data. In this article, we’ll provide an overview of what federated learning is, how it works, and what it’s used for. We’ll also explain the benefits and limitations of federated learning compared to traditional machine learning methods.