DEEP LEARNING -> FEDERATED LEARNING IN 10 LINES OF PYTORCH + PYSYFT
Summary: Simple code examples make learning easy. Here, we use the MNIST training task to introduce Federated Learning the easy way.
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So, why Federated Learning?
Federated Learning is a very exciting and upsurging Machine Learning technique for learning on decentralized data. The core idea is that a training dataset can remain in the hands of its producers (also known as workers) which helps improve privacy and ownership, while the model is shared between workers. One popular application of Federated Learning is for learning the "next word prediction" model on your mobile phone when you write SMS messages: you don't want the data used for training that predictor — i.e. your text messages — to be sent to a central server.
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Abschluss
As you observe, we only had to modify 10 lines of code to upgrade the official Pytorch example on MNIST to a real Federated Learning task!
https://blog.openmined.org/upgrade-to-federated-learning-in-10-lines/OpenMined is an open-source community focused on researching, developing, and promoting tools for secure, privacy-preserving, value-aligned artificial intelligence.
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