federated learning hierarchical

**Hierarchical Federated Learning** is a **multi-tier federated learning architecture that introduces intermediate aggregation layers** — instead of all clients communicating directly with a central server, clients first aggregate within local groups (e.g., within a site), then group aggregates are sent to the global server. **Hierarchical Architecture** - **Edge Level**: Devices/sensors within a single machine or department aggregate locally. - **Site Level**: Department-level models aggregate within a fab or facility. - **Global Level**: Site-level models aggregate at the organization or cross-organization level. - **Aggregation**: Each level can use different aggregation strategies (FedAvg, FedProx, robust aggregation). **Why It Matters** - **Communication**: Reduces long-distance communication — most aggregation happens locally. - **Scalability**: Scales to thousands of clients by distributing the aggregation load. - **Natural Structure**: Maps to organizational hierarchies (sensors → machines → fabs → enterprise). **Hierarchical FL** is **aggregation in tiers** — mirroring organizational structure for scalable, communication-efficient federated learning.