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

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Is this project already existing in the app?

• I have searched the existing projects

Tell us about the project

https://github.com/rwilliamspbg-ops/Sovereign_Map_Federated_Learning
Digital Public Good Highlights
Planetary-Scale Accessibility: While traditional AI infrastructure is centralized in massive data centers, this protocol enables 100M+ node orchestration on standard edge hardware. This democratizes AI development for researchers in resource-constrained environments who cannot afford centralized GPU clusters.

The "Seventh Theorem" for Human Rights: Unlike standard AI that "prunes" outliers for accuracy, our Dissensus Preservation logic prevents the automatic suppression of minority data. This protects the digital sovereignty of marginalized groups and ensures that "consensus" doesn't become a tool for cultural or intellectual erasure.

Extreme Resource Efficiency: By achieving a 700,000x reduction in metadata overhead, the protocol minimizes the carbon footprint of global AI training. This directly supports "Green AI" initiatives by allowing global collaboration over low-bandwidth, low-power mesh networks.

Is the Project a DPG (Digital Public Good)

• Yes
• No

Does the project list which Sustainable Development Goals it addresses in its labels?

• Yes
• No

Code of Conduct

• I've read the Code of Conduct and understand my responsibilities as a member of the GitHub community

Anything else?

Cybersecurity & Trust CredentialsByzantine Resilience ($55.5%$): Most decentralized systems fail if 33% of the network is compromised. Our Theorem 1 implementation provides a mathematical safety net for the "Truth" of the model even when the majority of nodes are under adversarial attack.Hardware-Anchored Ethics: By utilizing TPM 2.0 and Secure Enclaves, the project creates a verifiable "Chain of Trust" that prevents commercial or state capture of the "Genesis Nodes."Instant Verifiability: Our 10ms zk-SNARKs allow the network to prove mathematical correctness without ever seeing a user's private raw data, setting a new standard for privacy-preserving public infrastructure.