Federated learning can function effectively even when client connections are intermittent. The core design of federated learning allows clients (devices or servers) to participate in model training without requiring constant connectivity. Instead of relying on real-time communication, clients perform local training using their data and periodically send updates to a central server when they are online. This asynchronous approach accommodates devices that connect sporadically, such as mobile phones with unstable networks or IoT sensors with limited power.
The process typically works as follows: the central server initializes a global model and distributes it to available clients. Each client trains the model locally using its data, computes updates (e.g., gradient changes or weight adjustments), and sends these updates back to the server. If a client disconnects mid-training, it can resume or restart the process when reconnected. The server aggregates updates from all participating clients in each round, even if they join at different times. For example, a fitness app using federated learning could collect anonymized workout patterns from smartphones. Devices with poor cellular coverage could upload their updates hours or days later without disrupting the overall training process.
However, intermittent connectivity introduces challenges. Clients with delayed updates might contribute stale information, which could slow convergence or introduce noise. To mitigate this, techniques like weighted averaging (prioritizing recent updates) or limiting the age of accepted updates can help maintain model quality. Additionally, frameworks like TensorFlow Federated or Flower include built-in mechanisms to handle partial client participation. Developers can also implement client-side checkpointing to save progress if connections drop mid-training. While intermittent connections require careful handling, federated learning’s flexibility makes it viable for real-world scenarios where constant connectivity is unrealistic.