In federated learning, the server plays a crucial role as the central coordinator that manages the overall learning process without needing to access the raw data located on individual devices. Its primary responsibilities include aggregating model updates from various clients, orchestrating the training process, and ensuring that the individual contributions are safely combined to form a cohesive global model. While clients work with their local datasets, the server facilitates communication and synchronizes the updates to develop a robust machine learning model.
One specific task of the server is to receive model updates from different clients after they have trained their local models using their respective data. For example, if a bank uses federated learning across many branches to predict loan defaults, each branch's system will train the model using its local customer data. After training, each branch's model will send its updates back to the central server. The server then averages these updates, integrating the local insights without actually seeing the data used, thus preserving data privacy and security.
Additionally, the server is responsible for managing other elements of the federated learning process, such as scheduling when clients need to train or update, and monitoring the performance of the global model. For instance, it might decide to periodically send a new version of the global model back to each client for further training. This ensures that all clients work with the most updated version of the model while still protecting the sensitive user data stored on their devices. The server thus serves as the backbone of federated learning, enabling effective training while adhering to privacy requirements.