The number of clients in federated learning directly impacts performance through data diversity, communication efficiency, and computational resource allocation. Increasing the number of clients typically improves model generalization by exposing the global model to a broader range of data distributions. For example, training a language model across thousands of smartphones allows the model to learn regional dialects and usage patterns that a smaller set of clients might miss. However, more clients also mean higher communication costs, as the server must aggregate updates from more sources. This can slow down training rounds, especially if clients have limited bandwidth or intermittent connectivity.
The relationship between client count and performance isn’t linear. While a larger pool of clients reduces the risk of overfitting to specific datasets, it introduces challenges like stragglers—clients with slower hardware or unstable connections that delay aggregation. For instance, in a federated medical imaging system, hospitals with high-end GPUs might compute updates faster than clinics with older hardware, creating bottlenecks. Additionally, non-independent and identically distributed (non-IID) data becomes more pronounced with more clients. If 90% of clients in a facial recognition task lack diverse age groups, the global model may underperform for underrepresented demographics. Techniques like weighted aggregation or client clustering can mitigate this, but they add complexity.
Practical implementations often balance client count with resource constraints. Developers might limit participation per training round (e.g., selecting 100 clients out of 10,000) to reduce overhead. Frameworks like TensorFlow Federated or PyTorch Lightning optimize this by prioritizing clients with reliable connections or sufficient data. For example, a federated recommendation system might exclude devices with low battery to avoid incomplete updates. Adjusting the client participation rate dynamically—such as increasing it as the model stabilizes—can also improve efficiency. Ultimately, the optimal client count depends on the use case, infrastructure, and trade-offs between model accuracy and training speed.