clustered federated learning

**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.