Federated Learning

Klyff designs a distributed system where each manufacturing site runs a local inference and training environment on its own edge hardware or on-prem servers, connected to a secure central orchestrator that coordinates model updates without centralizing raw data.

The architecture accounts for network reliability, latency, and bandwidth constraints typical in industrial settings, utilizing an asynchronous model aggregation approach so that slow or offline sites do not impede the fleet's ability to learn.

This design enables scalability across dozens or hundreds of plants without building a costly centralized data lake.