Play 62
Federated Learning Pipeline
Very High✅ Ready
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
Azure Machine LearningConfidential ComputingBlob StorageKey VaultAzure Monitor
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
Differential privacy epsilonAggregation roundsMin participantsConvergence thresholdNoise multiplier
Estimated Cost
Dev/Test
$200–500/mo
Production
$5K–20K/mo