Centralized AI
All data flows to central cloud. Models trained on aggregated data. Single point of failure.
Federated Learning
Models stay local, gradients shared. Requires trusted aggregator. Synchronization bottleneck.
Edge AI
Processing on device. Full privacy. But isolated ā no learning from others.
| Attribute | Centralized AI | Federated Learning | Edge AI | QIS Protocol |
|---|---|---|---|---|
| Intelligence Scaling | ||||
| Privacy | ||||
| Single Point of Failure | ||||
| Communication Cost | ||||
| Latency | ||||
| Regulatory Compliance | ||||
| Network Value Growth | ||||
| Byzantine Fault Tolerance |
š” The Core Insight
Centralized systems compress N data points into one model.
QIS finds N(N-1)/2 pairwise patterns across N agents.
For 10,000 agents: Centralized gets 10,000 data points.
QIS gets 49,995,000 synthesis opportunities.
Distributed pattern finding is mathematically superior to centralized model training.
Architectural Comparison
š¢ Centralized / Federated
ā Single point of failure
ā Data leaves device (or gradients)
ā Linear scaling bottleneck
ā HIPAA/GDPR challenges
š QIS Protocol (P2P)
āļø Peer-to-peer via DHT (no coordinator)
ā No single point of failure
ā Data stays on device
ā Ī(NĀ²) intelligence scaling
ā Full regulatory compliance