What are the scalability issues in federated learning?

Federated learning faces several scalability challenges due to its decentralized nature, where models are trained across distributed devices or servers. One major issue is communication overhead. In federated learning, clients (e.g., mobile devices or edge servers) must send model updates to a central server, which aggregates them into a global model. As the number of clients grows, the volume of data transmitted increases exponentially, leading to network congestion and latency. For example, training a large neural network with millions of parameters across thousands of devices could require each client to upload updates frequently, straining bandwidth. Even with techniques like model compression or selective client participation, coordinating updates across a massive client pool remains a bottleneck, especially in environments with unreliable or slow connections.

Another challenge is heterogeneity in client capabilities and data distribution. Clients vary in computational power, storage, and energy constraints. For instance, smartphones might have different hardware specs, battery levels, or availability windows. Training on low-power devices can slow convergence, as some clients may take longer to compute updates or drop out mid-training. Data heterogeneity also poses a problem: clients often have non-identically distributed (non-IID) data, which can bias the global model. For example, a federated healthcare model trained on hospital data might perform poorly if one hospital specializes in rare diseases while others focus on common ailments. Scaling requires balancing these disparities, often through techniques like adaptive client selection or personalized model variants, but these solutions add complexity.

Finally, coordination and synchronization become harder as the system scales. Federated learning frameworks typically rely on synchronous aggregation, where the server waits for all clients to submit updates before proceeding. With thousands of clients, this approach is impractical due to stragglers—clients that lag behind due to slow hardware or connectivity. Asynchronous methods avoid waiting but risk stale updates, where older client contributions conflict with newer model versions. For example, a client training on an outdated global model might send updates that destabilize the aggregated result. Mitigating this requires careful design, such as setting timeouts for updates or weighting contributions based on staleness, but these tweaks can reduce model accuracy or increase infrastructure costs. Balancing scalability with reliable training remains an open problem.