Federated Learning

Federated Learning enables multiple organizations to jointly train fraud detection and risk models by sharing model updates—not raw data. Each party trains locally on their identity/transaction data, then aggregates encrypted gradient updates into a global model. This approach preserves data sovereignty, complies with privacy regulations, and enables collective defense against identity fraud without exposing proprietary datasets.