I just finished testing a federated learning system at 10 million nodes.

It maintains 82% accuracy even when 5 million nodes are malicious.

Here's what happened ↓

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*The Test (Feb 24, 2026)*

10,000,000 nodes 4,000,000 - 5,000,000 malicious (Byzantine) nodes 59 minutes 41 seconds total runtime 100% success rate

Results: • 40% Byzantine (4M bad): 83.3% accuracy • 50% Byzantine (5M bad): 82.2% accuracy

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*Why this matters*

Google's federated learning papers max out at ~10K nodes in production.

Academic Byzantine fault tolerance systems (HoneyBadgerBFT, etc.) are tested at 100-1K nodes.

I just validated 10M nodes with 50% malicious participation—solo, in under an hour.

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*Scaling proven across 5 orders of magnitude*

100 nodes → 10M nodes O(n log n) holds perfectly Streaming aggregation prevents memory death Per-round time: 127-154 seconds at 10M scale

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*The stack*

- Rust/Go core (MOHAWK protocol) - Python SDK - WebAssembly edge runtime - zk-SNARK verification (<1ms) - Hardware root of trust (TPM 2.0) - Hierarchical batching for extreme scale

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*Solo dev context*

Built this alone. 5 hours of continuous testing today. 135KB documentation. 100% test pass rate.

No $10M venture funding. No PhD team. No Google infrastructure.

Just code that works at any scale.

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*What this enables*

- Global sensor networks (climate, defense, agriculture) - Cross-hospital AI without patient data sharing - Multi-national intelligence collaboration - Autonomous vehicle fleets training together - Any scenario where you can't trust 50% of participants

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Release: https://github.com/rwilliamspbg-ops/Sovereign_Map_Federated_...

Repo: https://github.com/rwilliamspbg-ops/Sovereign_Map_Federated_...

Looking for: defense pilots, enterprise users, academic collaboration, contributors.

Happy to answer questions.

Show HN: I solo-validated Fed learning at 10M nodes with 50% Byzantine tolerance