Federated learning presents several computational overheads that developers should be aware of when implementing this approach. One major overhead comes from the need for local computations on client devices. Each device must train a local model using its own data before sending updates back to a central server. This requires processing power and energy, which can be particularly taxing for low-resource devices like smartphones or IoT gadgets. For example, if a million devices each need to perform several iterations of model training, the cumulative computational burden can become substantial.
Another key overhead is related to communication. After local training, the model updates (typically gradients) must be sent to the central server. If the updates are large or if there are many participating devices, this can lead to significant network traffic, potentially causing delays. This asynchronous communication can also impact the synchronization between the server and clients. In cases where devices operate on unstable connections, data may be lost, requiring additional rounds of training and communication to resolve, which further escalates resource use.
Lastly, federated learning can introduce overhead in terms of model aggregation and management. The server must efficiently combine updates from various clients, which can be computationally intensive, especially as the number of devices grows. For instance, techniques such as secure aggregation, which ensure that the updates are combined in a privacy-preserving manner, may require additional computational resources. Moreover, dealing with heterogeneous devices—variations in hardware and data availability—adds complexity to the model training and may necessitate more frequent adjustments and fine-tuning. This can stretch computational resources even further, making it crucial for developers to architect federated learning systems with these challenges in mind.