In federated learning, data is distributed across multiple devices or locations rather than being centralized in a single server or database. Each participating device—such as smartphones, tablets, or edge servers—stores its own local data, which could include user interactions, sensor data, or other forms of information. This decentralized approach allows the training of machine learning models to take place directly on these devices, while keeping the data local and private. Only the model updates or gradients—essentially the changes needed to improve the model—are sent to a central server, instead of the raw data itself.
For example, consider a federated learning scenario involving users of a mobile app designed for health tracking. Each app collects personal health metrics from its users, like steps taken or heart rate. Instead of sending this sensitive information to a central server, each device computes changes to its local model based on the health data it holds. The results—such as how much the model has learned—are sent back to the server. The central server aggregates these updates from many devices to improve a global model that can then be shared back to the devices without ever exposing any individual user’s data.
This approach not only enhances privacy and security but also utilizes the computational power of distributed devices effectively. Developers working with federated learning must implement mechanisms that ensure the efficiency of the communication between devices and the server while minimizing the amount of data transmitted. This includes techniques for secure aggregation, differential privacy, and a robust framework for model updates, ensuring that the collective learning process remains effective and resilient against data leakage.