Federated averaging is a key method in the area of federated learning, which allows multiple devices or clients to collaboratively train a machine learning model without sharing their local data. The primary role of federated averaging is to enable the aggregation of updates from different participants, so a global model can be trained while keeping individual datasets private. This approach is particularly useful in scenarios where data privacy is a concern, such as in healthcare or finance, where sensitive information cannot be shared directly.
The process begins when a central server sends a copy of the current global model to multiple clients, like smartphones or IoT devices. Each client performs local training on its own data using this model, improving it based on the patterns found in their datasets. Once the local training is complete, each client sends only the model updates—such as weight changes or gradients—back to the central server, instead of sharing the actual data. The server then averages all these updates to create a new global model. This averaging process ensures that the central model benefits from the knowledge captured across all clients while maintaining the privacy of their individual data.
For example, consider a scenario where a group of hospitals wants to improve a predictive model for patient diagnosis. Each hospital trains its model using its patient data and shares the model updates through federated averaging. By doing this, each hospital contributes to a more robust global model that captures diverse patient patterns without compromising privacy. As a result, federated averaging not only enhances model performance across varied datasets but also addresses the critical concerns of data security and compliance in sensitive industries.