What are client devices in federated learning?

Client devices in federated learning are the decentralized endpoints—such as smartphones, IoT sensors, or edge servers—that collaboratively train a machine learning model without sharing raw data. These devices hold local datasets and participate in the training process by computing updates to a global model provided by a central server. For example, a smartphone might use its user’s typing history to improve a keyboard app’s prediction model, while a sensor in a factory could analyze equipment data to detect anomalies. The key feature is that data remains on the device, preserving privacy and reducing the need to transmit sensitive information.

The role of client devices involves three main steps: downloading the global model, computing updates locally, and returning those updates to the server. First, the server sends the current model version to selected devices. Each device then trains the model using its local data, adjusting parameters via methods like stochastic gradient descent. For instance, a fitness tracker might train a model to predict user activity based on motion data stored on the device. Once training is complete, the device sends only the model updates (e.g., gradients or weights) back to the server. This approach minimizes data exposure—a hospital’s MRI machines, for example, could contribute to a diagnostic model without sharing patient scans.

Practical challenges include device heterogeneity, connectivity limitations, and data variability. Client devices vary in hardware (e.g., high-end phones vs. low-power sensors), which affects training speed and resource availability. A federated learning system must handle devices that drop offline mid-training or have intermittent connectivity. Additionally, data across devices may be non-IID (non-independent and identically distributed)—a user’s smartphone might have unique typing patterns, while another’s has none. Techniques like federated averaging aggregate updates while tolerating missing participants, and adaptive sampling prioritizes reliable devices. Secure aggregation protocols further ensure updates cannot be traced to individual devices, addressing privacy concerns in scenarios like cross-organizational collaboration.**