cross-silo federated learning

**Cross-Silo Federated Learning** is a **federated learning setting where a small number of organizations (2-100) collaborate to train a model** — each organization (silo) has a reliable compute infrastructure, large local datasets, and participates in every training round. **Cross-Silo Characteristics** - **Few Participants**: Typically 2-100 organizations (hospitals, fabs, banks). - **Reliable**: All participants are always available — synchronous training is feasible. - **Large Local Data**: Each silo has substantial local datasets (unlike cross-device FL). - **Governance**: Formal agreements, contracts, and compliance requirements between participants. **Why It Matters** - **Industry Collaboration**: Multiple semiconductor fabs can jointly train defect classifiers without sharing proprietary data. - **Regulatory**: Each organization keeps data within its regulatory jurisdiction (GDPR, export controls). - **High Value**: Each silo contributes unique, high-value data — collaboration yields significantly better models. **Cross-Silo FL** is **organizational collaboration** — a few large organizations jointly learning from their combined knowledge without sharing raw data.