Federated learning
Okay kiddo, let’s learn about federated learning!
Imagine your teacher wants to know how well you and your classmates are doing in math. Instead of having you all come to the front of the class to share your answers, she wants to ask one person in each group to collect everyone's answers and bring them back to her.
That's kind of what federated learning is like, except it's used with computers instead of people.
Basically, companies and organizations create computer programs called "models" that can learn things and make predictions about data. With federated learning, instead of sending all the data to one central location to train the model, the data stays on individual devices or in different locations.
Each device or place runs the model on its own data, and then shares the updated model with a central location. Then, the models are combined and updated, so that everyone can benefit from the updated model.
Think of it like a puzzle. Imagine everyone has one puzzle piece, and when they put it together, it forms a big picture. The updated model is like the completed puzzle, but you can only see one puzzle piece at a time. By working together, everyone can make a more complete picture.
Federated learning is especially useful when the data is sensitive, personal, or confidential. For instance, your phone can use federated learning to learn what you like to type without sending your actual messages to a server where others might be able to see them.
I hope this explanation helps you understand federated learning, kiddo!
Imagine your teacher wants to know how well you and your classmates are doing in math. Instead of having you all come to the front of the class to share your answers, she wants to ask one person in each group to collect everyone's answers and bring them back to her.
That's kind of what federated learning is like, except it's used with computers instead of people.
Basically, companies and organizations create computer programs called "models" that can learn things and make predictions about data. With federated learning, instead of sending all the data to one central location to train the model, the data stays on individual devices or in different locations.
Each device or place runs the model on its own data, and then shares the updated model with a central location. Then, the models are combined and updated, so that everyone can benefit from the updated model.
Think of it like a puzzle. Imagine everyone has one puzzle piece, and when they put it together, it forms a big picture. The updated model is like the completed puzzle, but you can only see one puzzle piece at a time. By working together, everyone can make a more complete picture.
Federated learning is especially useful when the data is sensitive, personal, or confidential. For instance, your phone can use federated learning to learn what you like to type without sending your actual messages to a server where others might be able to see them.
I hope this explanation helps you understand federated learning, kiddo!