Introduction To Federated Learning: Enabling The Scaling Of Machine Learning Across Decentralized Data Whilst Preserving Data Privacy

Large volumes of data are required for training machine learning models. The trained model is run on a cloud server that users can access through various applications such as web search, translation, text production, and picture processing, which is the standard procedure for establishing machine learning applications.

The application must transfer the user’s data to the server where the machine learning model is stored every time it wishes to use it, creating privacy, security, and processing issues.

Fortunately, developments in …

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