Federated learning addresses device heterogeneity—differences in hardware, compute power, or connectivity across participating devices—through strategies that decouple the training process from individual device limitations. Instead of requiring uniform hardware or consistent availability, the system adapts to varying device capabilities by allowing asynchronous participation, flexible model updates, and resource-aware optimization. For example, a smartphone with limited battery might contribute smaller, less frequent updates, while a server with more resources could handle larger batches. This flexibility ensures that diverse devices can collaborate without being bottlenecked by the weakest link.
One key technique is adaptive model aggregation. The central server collects updates from devices but weights their contributions based on factors like data quality or compute capacity. For instance, a device with a powerful GPU might train on more data points per round, so its updates could be prioritized during aggregation. Conversely, slower devices might submit partial updates or use compressed models (e.g., via quantization) to reduce communication overhead. Frameworks like TensorFlow Federated also allow developers to define custom aggregation logic, enabling fine-grained control over how heterogeneous updates are combined. Additionally, some systems use client selection algorithms to prioritize devices with sufficient resources for a given training round, avoiding stragglers that could delay convergence.
Another approach involves optimizing for varying hardware via dynamic model architectures. For example, a federated system might deploy smaller, lightweight models (e.g., MobileNet) to low-memory devices while allowing more capable devices to train larger models. Techniques like knowledge distillation can then reconcile these differences by transferring insights from larger models to smaller ones. Alternatively, frameworks such as Flower allow clients to request model parameters tailored to their compute constraints. On the data side, federated learning handles skewed data distributions (common in heterogeneous devices) by using normalization techniques or personalized layers that adapt global models to local data patterns. By combining these methods, federated learning ensures that diverse devices contribute meaningfully without requiring uniform hardware or forcing developers to compromise on model complexity.