FEDERATED_AI
Privacy-Preserving Distributed Model Training
Train AI models across hospitals, banks, or factories without moving sensitive data. Data stays local, meets regulations, and coordination happens in milliseconds through Hyperweave's geo-intelligent mesh.
Federated Learning Network
Distributed model training with local data privacy — gradients flow through geographic aggregators
EPOCH_1
FLACCURACY
72.0%
LOSS
0.450
NODES
6
NETWORK_LOG
DATA_SOURCES
DATA_FLOW
DATA_LOCALITY_GUARANTEE
Sensitive data never leaves its origin node. Model gradients are exchanged via Hyperweave's encrypted P2P channels, ensuring HIPAA, GDPR, and industry compliance.
GEOGRAPHIC_AGGREGATION
Model updates are aggregated by geographic proximity first, reducing cross-continent bandwidth by orders of magnitude. Regional updates merge before global synchronization.
ADAPTIVE_CONSENSUS
Hyperweave's distributed consensus ensures all participating nodes agree on model state without a central coordinator. Byzantine fault tolerance protects against malicious participants.
ELASTIC_PARTICIPATION
Nodes can join or leave the training mesh without disrupting others. The self-healing topology automatically rebalances workloads and routing paths.
Gradient Exchange
< 50ms
Consensus Round
< 100ms
Node Discovery
< 10ms
Fault Recovery
O(1)
Max Participants
Unlimited
Encryption
E2E AES-256
Healthcare
Train diagnostic AI across hospital networks while keeping patient data on-premise. Meet HIPAA requirements without sacrificing model accuracy.
Financial Services
Collaborate on fraud detection models across banks without sharing transaction data. Regulatory compliance built into the protocol layer.
Manufacturing
Share predictive maintenance insights across factories without exposing proprietary processes. Improve equipment uptime industry-wide.