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With the explosive growth of data, it is of great strategic importance to analyze and mine the potential value of data. Machine learning is an important tool for enabling data analysis and mining. However data often contains sensitive information. Therefore the collection of sensitive data poses significant risks and is often prohibited. Thus preventing the value of large amounts of data from being realized. Federated learning is a field of training ML model. FL algorithms typically iterate training rounds, during which model updates are typically aggregated at the server after local training. This approach holds great promise for leveraging the value of data while protecting data privacy. This pair of pictures depicts federaled learning. The client sends only the gradients derived from training with its own data to the server. |
IEEE/ICACT20220347 Slide.04
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