Federated Learning at the Edge: The Architecture Behind Vigil's Privacy Design
The federated learning architecture we're designing into Vigil — why on-device training matters for industrial AI where raw data never leaves the facility.
As autonomous systems scale, the traditional model of centralizing data for model training becomes both a bottleneck and a liability. Data sovereignty, compliance constraints, and the sheer bandwidth required to move industrial telemetry off-site necessitate a paradigm shift.
This is the architectural thinking behind Vigil's planned privacy model: Federated Learning (FL) at the edge, where model improvement happens without raw data ever leaving the facility.
The Architecture of Decentralized Intelligence
Instead of moving data to the model, we move the model to the data.
- Local Training: Each edge node (whether an industrial sensor, a robotic unit, or a local server) trains a local model using its proprietary data stream.
- Weight Aggregation: Only the model weights (the learned parameters) are encrypted and transmitted back to the central aggregator.
- Global Consensus: The central server aggregates these weights using Secure Multi-Party Computation (SMPC) to form an updated global model, which is then redistributed.
Benefits in Production
- Zero Data Exposure: Raw production data never leaves the facility, ensuring absolute compliance with NDAs and data privacy regulations.
- Reduced Latency: Edge devices adapt to local environments without waiting for cloud-based retraining cycles.
- Bandwidth Efficiency: Transmitting model weights requires a fraction of the bandwidth compared to streaming raw video or sensor feeds.
Federated Learning is not just an academic concept — it is the privacy architecture we are designing Vigil around from day one.