Play 62
Federated Learning Pipeline
Privacy-preserving distributed training across data silos without sharing raw data.
Federated learning orchestration on Azure — train models across multiple organizations or data silos while keeping raw data in place. Uses Azure Confidential Computing enclaves for secure aggregation, differential privacy guarantees configurable per participant, model convergence monitoring, and cross-organization collaboration protocols. Purpose-built for healthcare, finance, and government scenarios where data sovereignty is non-negotiable.
Architecture Pattern
Distributed training: secure aggregation, differential privacy, convergence-gated promotion
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — FL Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (244 lines), evaluate (110 lines), tune (196 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI key input + envFile
TuneKit (AI Config)
- config/openai.json — gpt-4o for convergence analysis
- config/guardrails.json — strict privacy, differential privacy budget
- evaluation/eval.py — Convergence <10 rounds, Privacy budget <1.0
Tuning Parameters
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.