FedAvg simplified

Federated learning is a machine learning technique that enables multiple devices, such as smartphones or edge devices, to collaboratively train a model while keeping their data on the devices and never sending it to a central server. This approach has several advantages, such as privacy protection and the ability to train models on a much larger dataset that is distributed across devices.

One popular method for implementing federated learning is called federated averaging, or FedAvg for short. In FedAvg, the goal is to train a model that can accurately predict a target variable based on a set of input features, but the data for training the model is distributed across a large number of devices.

Here’s how FedAvg works:

The main advantage of FedAvg is that it allows for collaborative model training without requiring any device to send its data to a central server. This makes it well-suited for scenarios where data privacy is a concern, such as in healthcare or finance.

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