Clustered Federated Learning is a federated learning approach that groups clients into clusters with similar data distributions — training separate models for each cluster instead of one global model, achieving better personalization while maintaining the benefits of collaboration within each cluster.

Clustering Methods

• Gradient-Based: Cluster clients by the similarity of their gradient updates — similar gradients = similar data.
• Loss-Based: Cluster based on cross-client loss evaluation — assign clients to the cluster whose model fits them best.
• Iterative: Alternate between training cluster models and reassigning clients to clusters.
• Hierarchical: Multi-level clustering for fine-grained grouping.

Why It Matters

• Non-IID Handling: One global model struggles with highly diverse data — clusters capture sub-population structure.
• Semiconductor: Different fabs or product lines may form natural clusters — each cluster gets an optimized model.
• Privacy: Clustering is done based on model updates, not raw data — privacy is maintained.

Clustered FL is finding the tribes — grouping similar clients together for better models while maintaining federated privacy.