FEDERATED_LEARNING_COORDINATION
The discovery + checkpoint plane for 10,000 sites that won't move their data
Federated learning (FedAvg, FedProx, FedNova) lets hospitals, banks, factories, and mobile devices train shared models locally and exchange only gradient updates — keeping raw data on-premise. Existing frameworks (Flower, PySyft, FATE) ship weak coordination layers that crumble past hundreds of clients. Hyperweave is the missing coordination plane: participant discovery, aggregator placement, contribution tracking, and checkpoint distribution at O(k + log n) per node. (Your secure-aggregation crypto handles the math; Hyperweave handles the choreography.)
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
COORDINATION_PLANE_NOT_GRADIENT_BUS
Hyperweave is not the bandwidth-bound gradient bus (NVLink, datacenter fabrics own that for frontier training). It IS the layer that locates participants, elects aggregators, tracks contributions, and distributes checkpoints across thousands of sites without a central coordinator.
DATA_RESIDENCY_BY_CONSTRUCTION
Region tags on descriptors make jurisdiction a routing-layer property — replicas literally cannot land outside an approved region. EU-only, India-only, HIPAA-bound deployments enforce residency structurally, not via policy that can be misconfigured.
GEO_LOCAL_AGGREGATION
Hilbert encoding means gradient chunks aggregate by geographic proximity first — regional aggregators merge before any cross-continent synchronization. Bandwidth bills drop with the locality, not with hand-tuned topology scripts.
ELASTIC_PARTICIPATION
Clients drop in and out between rounds; the registry self-heals via SWIM-style liveness signals and 3-state suspect/dead semantics. New participants discover the round at a rendezvous anchor — no manual roster maintenance.
Median latency
4.65× faster vs top DHTs
Tail latency (p99)
5× faster vs top DHTs
Fault recovery
3× faster
Per-node state
O(k + log n)
Max participants
1M target
Identity
Ed25519 / X25519
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.