Communication in federated learning (FL) between the server and clients is primarily handled through a decentralized approach. In this setup, clients (devices or nodes) perform local training on their own data and periodically communicate their model updates to a central server. This process typically involves sending aggregated model information rather than raw data, which helps maintain user privacy and data security. The server collects these updates from multiple clients, averages or aggregates them, and then shares the updated global model back to the clients for further training.
For example, in a scenario where mobile devices participate in federated learning for predictive text, each device trains a model on its own user data, such as typing patterns. After a certain number of iterations, each device computes its model updates (such as weight adjustments) and sends them back to the server. The server does not receive the raw typing data but rather the updates that improve the global model. This step is crucial, as it limits the exposure of sensitive user data while still benefiting from collective learning from diverse data sources.
Furthermore, communication can be optimized to reduce bandwidth usage and latency. Techniques like quantization or compression can be applied to the model updates before sending them to the server. Additionally, secure aggregation methods can be utilized to ensure that clients' contributions remain private during the update process. By managing the communication efficiently, federated learning enables robust model training while keeping user data safe and ensuring that the learning process is scalable across many clients.