How does federated learning handle unbalanced data distributions?

Federated learning handles unbalanced data distributions by adapting how models are trained and aggregated across clients. In federated settings, data imbalance can occur in two ways: (1) individual clients may have skewed class distributions (e.g., one client has 90% of class A data), or (2) clients themselves vary greatly in data size (e.g., one client has 10,000 samples, another has 100). The system addresses these issues through algorithmic adjustments and aggregation strategies rather than centralizing data. For example, a hospital in a healthcare federated network might have far more cancer cases than rare diseases, while another clinic has the reverse. The global model must learn from both without assuming uniform data distribution.

One common approach is weighted aggregation during the server’s model update phase. Instead of averaging client model updates equally, the server assigns weights based on each client’s data size or class distribution. For instance, a client with 1,000 samples might contribute twice as much to the global model as one with 500 samples. To handle class imbalance within a client, techniques like class-aware sampling or loss reweighting can be applied locally. Developers might implement this by modifying the training loop on each client to compute loss with class-specific weights (e.g., penalizing errors on rare classes more heavily). Frameworks like TensorFlow Federated or PyTorch’s Substra provide built-in tools for such adjustments, allowing developers to customize client-side training without reinventing the wheel.

Advanced methods include dynamic client selection and personalized federated learning. For example, if rare classes are concentrated in a few clients, the server might prioritize sampling those clients more frequently during training rounds. Alternatively, personalized approaches let clients fine-tune the global model locally to better fit their unique data distribution. A practical implementation could involve sending a base model to all clients, then allowing each to apply a few additional layers trained only on their data. This balances shared knowledge with local adaptation. Libraries like FedML or Flower support these strategies through configurable client/server workflows. While no single method eliminates imbalance entirely, combining these techniques helps mitigate bias and improves model robustness across diverse data sources.