Description: Device aggregation in the context of federated learning refers to the process of combining model updates from multiple devices to create a single improved model. This approach allows devices to collaborate in training artificial intelligence models without the need to share sensitive data. Each device trains a local model using its own data, and instead of sending this data to a central server, it only sends the model updates. This not only preserves data privacy but also reduces the need for bandwidth and storage on the server. Aggregation is performed using algorithms that combine local model updates, thereby optimizing the performance of the global model. This method is particularly relevant in environments where data is distributed and may be sensitive, such as in mobile devices, health applications, and IoT systems. Device aggregation enables continuous improvement of the model as more devices participate in the process, resulting in a more robust and accurate model without compromising user privacy.
