In federated learning, updates are synchronized through a process that involves aggregating model updates from multiple devices without sharing raw data. Each participating device, such as a smartphone or IoT sensor, trains a local copy of the model using its own data. Once training is complete, each device sends its model updates—typically the weights and biases of the neural network—to a central server. This method ensures that sensitive data remains on the device and is not transmitted, thereby enhancing privacy.
The central server plays a crucial role in combining these updates. After collecting the model updates from devices, the server aggregates them using a method like Federated Averaging. This involves averaging the weights from all participating devices, which generates a new global model that reflects the knowledge learned from diverse datasets across devices. For example, if three devices send their updated model parameters, the server calculates the average of these parameters to produce a single updated model. This aggregated model is then sent back to the devices, allowing them to continue training with the most current version without redistributing individual user data.
Synchronization also requires careful management of communication and timing. To avoid issues like stale updates, the system may enforce a schedule for when devices should send their updates and receive the aggregated model. Techniques such as asynchronous updates or coordinated training rounds can help in managing this process effectively. For instance, some devices might finish their local training earlier than others, so they can send updates right away, while a batch of updates from slower devices can be processed later. Such strategies ensure that the model remains up-to-date and that training can proceed collaboratively across all contributing devices.