DEV Community: Ryan

Join the Mesh: Byzantine-Tolerant FL is Here!

Ryan — Tue, 03 Mar 2026 01:02:32 +0000

I’ve just reached a major milestone with Sovereign Map, an AI-native federated learning framework designed for edge sovereignty and massive scale. 🌐

We’re moving away from centralized data monopolies and building a decentralized neural mesh where:

Privacy is Default: Your data never leaves your device.

Byzantine Fault Tolerance: The network stays secure even with malicious actors.

Massive Scaling: Orchestrate 100M+ nodes on standard hardware.

We are looking for developers, researchers, and tech enthusiasts to help us harden the protocol and explore the future of decentralized intelligence.

Get Involved:
Star the Repo: Check out the source on GitHub.

Join the Community: Let’s talk architecture on r/SovereignMap.

Deploy a Node: Launch your own instance in 5 minutes using our Quick Start Guide.

The future isn't centralized—it's sovereign. Let's build it together. 🤖🛡️

FederatedLearning #OpenSource #Web3 #CyberSecurity #MachineLearning

Sovereign_Map: Building a Byzantine-Tolerant Future for Edge Computing

Ryan — Fri, 27 Feb 2026 16:56:01 +0000

This is a submission for the DEV Weekend Challenge: Community

The Community

What I Built

Demo

https://github.com/rwilliamspbg-ops/Sovereign_Map_Federated_Learning

Code

How I Built It

Sovereign Mohawk: Formally Verified Federated Learning at 10M-Node Scale

Ryan — Wed, 25 Feb 2026 19:00:02 +0000

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.

Core Problems Solved !

Ryan — Tue, 24 Feb 2026 15:57:45 +0000

Core Problems Solved:

View the SMP Repository on GitHub

Security Vulnerabilities at Scale
Traditional Federated Learning (FL) systems often fail when 33% of nodes are malicious. SMP is mathematically guaranteed to be resilient against Byzantine attacks even if up to 55.5% of nodes are compromised.

Communication Bottlenecks:

SMP optimizes efficiency by significantly reducing complexity. This reduces metadata overhead by 700,000x (e.g., shrinking data requirements from 40 TB down to 28 MB for 10 million nodes).

Trust and Verification
SMP eliminates the need to trust a central aggregator by using zk-SNARK proofs.

Size: 200-byte proofs

Speed: 10ms verification

Benefit: Allows for massive updates without the need for re-execution.

Data Sovereignty & Privacy
It addresses "data rent" issues by ensuring raw data never leaves the edge device. It utilizes Differential Privacy (DP) with a verifiable "Privacy Budget" to prevent individual data leakage.

Resource Constraints on Edge Devices
Optimized for low-power hardware (NPUs) on mobile and IoT devices, the system stabilizes at approximately 2.72 GB of RAM during 10-million-node simulations.

Specific Application Use Cases
Decentralized Spatial Intelligence: Creating privacy-safe Sovereign Maps (e.g., LiDAR mapping) where updates are shared but private location data remains local.

Green AI Infrastructure: Moving AI training from power-hungry data centers to a decentralized "edge" network of low-power home devices.

Universal Basic Compute Economy: Allowing node operators to earn rewards for contributing compute power and data without sacrificing ownership.

Private AI Agents: Enabling developers to build secure AI agents using the Python SDK that can learn from personal data locally.