Edge-native federated intelligence protocol
Autonomous AI nodes communicating through peer-to-peer mesh networks. Powered by the Mohawk Proto reference runtime— Go, Wasmtime, and TPM-secured edge pipelines. No central authority. No single point of failure.
Your data never leaves your device. Training happens at the edge. Models learn, but secrets remain sealed. The Mohawk Proto runtime ensures TPM-backed attestation for every computation.
No single point of failure. If a node falls, the network adapts. Gossip protocols ensure knowledge propagates like neural impulses through a digital nervous system. Mobile Offloading and Heterogeneous Adaptive Weights (MOHAWK) optimize every edge.
TPM stubs and secure enclaves provide cryptographic proof of execution. WebAssembly sandboxing isolates untrusted code. The Mohawk reference implementation proves the security model—tiny, fast, unbreakable.
Mobile Offloading and Heterogeneous Adaptive Weights for Knowledge. The reference node agent runtime proving the Sovereign Map security model.
Mohawk Proto demonstrates that federated learning can operate securely on resource-constrained edge devices. The TPM stub provides hardware-backed identity and attestation, while Wasmtime ensures memory-safe execution of untrusted model code. A tiny FL pipeline proving that security and efficiency need not be trade-offs.
Real-time visualization of the Sovereign Map network topology. Each node represents an autonomous learning agent running Mohawk runtime.
Five layers of decentralized intelligence. Each layer operates autonomously while contributing to collective network wisdom. Mohawk Proto implements the edge layer.
Federated learning applications. Medical diagnostics, autonomous vehicles, IoT intelligence. Domain-specific model training without data centralization.
Decentralized Stochastic Gradient Descent (DSGD) with Byzantine fault tolerance. Krum and Median aggregation algorithms ensure model integrity against malicious actors.
Peer-to-peer gossip protocols with dynamic topology adaptation. Device-to-device (D2D) communication enables operation without internet backbone. Handles intermittent connectivity and node churn.
Differential privacy guarantees and secure multi-party computation. Homomorphic encryption for encrypted model updates. Zero-knowledge proofs verify computation integrity.
Local model training on edge devices secured by TPM attestation. Mohawk Proto provides the reference implementation: Go runtime + Wasmtime sandbox + hardware root of trust. Battery-aware training schedules for mobile devices with WASI-compliant WebAssembly modules.
The future is not centralized. It is sovereign, distributed, and intelligent. These are the pillars of tomorrow's decentralized AI infrastructure.
Entire urban centers operating as unified federated learning networks. Streetlights, vehicles, and infrastructure communicating via mesh protocols, creating city-scale neural networks that optimize traffic, energy, and safety in real-time without central data collection.
Global scientific collaboration without data sharing. CERN, NASA, and research institutions training unified models across continents. Particle physics, climate modeling, and genomic research accelerated by collective intelligence while preserving institutional data sovereignty.
The Mohawk Proto runtime becomes the industry standard for secure edge AI. TPM-backed attestation mandatory for critical infrastructure. WebAssembly sandboxing universal. Hardware roots of trust in every IoT device, from medical implants to autonomous drones.
Post-quantum cryptographic protocols securing federated learning against quantum adversaries. Quantum key distribution (QKD) for model update encryption. Hybrid classical-quantum networks achieving unprecedented computational parallelism for AI training.
Next-generation fault tolerance eliminating 90%+ of attack vectors. AI-driven anomaly detection identifying malicious nodes in <10ms. Self-healing networks that automatically isolate threats and reconfigure topology to maintain consensus integrity under active attack.
Federated learning across planetary distances. Mars colonies training models with Earth via delay-tolerant networking (DTN). Satellite mesh networks enabling AI coordination for space exploration without real-time Earth dependency. True autonomy for off-world intelligence.
The future of AI is being written today. Deploy a Mohawk node, contribute to the protocol, or join our Reddit community to shape the next generation of decentralized intelligence.