In federated learning, computation is offloaded by distributing training tasks across multiple devices or servers while keeping data localized. Instead of centralizing data on a single server, each participating device (or “client”) trains a model locally using its own data. These local models are then sent to a central server, which aggregates them into a global model. This approach shifts the bulk of computational work—such as gradient calculations and parameter updates—to the clients, reducing the server’s workload to aggregation and coordination. By design, this preserves data privacy and reduces bandwidth usage, as raw data never leaves the devices.
A common example is training a keyboard prediction model on smartphones. Each phone trains a lightweight model using the user’s typing history. The local model updates (e.g., gradients or weights) are sent to a central server, which averages them to create an improved global model. The server then distributes the updated model back to the devices for further training. Frameworks like TensorFlow Federated or PyTorch’s FL tools simplify this process by providing APIs to define client-side training logic and server-side aggregation. For instance, a developer might implement a client_update function that runs stochastic gradient descent (SGD) on a device and a server_update function that applies Federated Averaging (FedAvg) to combine results.
Challenges in offloading computation include handling device heterogeneity and optimizing communication. Devices vary in compute power, so training times may differ significantly. To address this, techniques like adaptive client selection or asynchronous aggregation can prioritize faster or more reliable devices. Communication overhead is reduced by compressing model updates (e.g., using quantization) or limiting update frequency. For example, a medical imaging application might train on hospital servers (not individual devices) to leverage higher compute resources while still keeping patient data decentralized. Secure aggregation protocols can also mask updates to prevent the server from reverse-engineering sensitive information. These strategies ensure efficient, privacy-preserving offloading without compromising model performance.