I'm excited to share Sovereign Mohawk, a high-performance, formally verified federated learning (FL) architecture designed to solve the "trust-at-scale" problem.Traditional FL systems often hit a wall due to communication bottlenecks and security vulnerabilities. SMP introduces a hierarchical synthesis model capable of supporting 10 million nodes while ensuring local data never leaves the edge device.💡 Key InnovationsPlanetary Scale Communication: We’ve reduced communication complexity from $O(dn)$ to $O(d \log n)$. In stress tests, this dropped metadata overhead from 40 TB down to just 28 MB.Industry-Leading Byzantine Resilience: SMP remains mathematically secure even if 55.5% of nodes are adversarial.zk-SNARK Verification: Global updates are verified in ~10ms using 200-byte proofs, removing the need for a "trusted" central server.Performance-First SDK: A zero-copy ctypes bridge between the Go 1.24 core and Python SDK provides raw speed with Pythonic ease of use.🛠️ Tech StackRuntime: Go + Wasmtime (for secure execution on any edge hardware).Security: Groth16 zk-SNARKs and Rényi Differential Privacy ($ε = 3.88$).Hardware: Integrated TPM capability-scoped interfaces.🔗 Links & ResourcesMain Repo: Sovereign-Mohawk-ProtoDocumentation: Check out SDK_USAGE.md in the repo.Live Site: Sovereign Mohawk Proto WebWhat do you think? I’m looking for feedback on the Theorem 5 logic and edge engineers interested in porting the node-agent to NPU-heavy hardware like NVIDIA Jetson.
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Thanks for checking out the project! For those interested in the math behind the 55.5% Byzantine resilience, I’ve documented the logic in the README.md. If you want to see how the Go core interfaces with the Python SDK, check out SDK_USAGE.md. Happy to answer any questions on the $O(d \log n)$ scaling implementation!