Play 62 β Federated Learning Pipeline
Privacy-preserving distributed training β FedAvg server orchestration, client local training (data never leaves), differential privacy via Opacus, optional secure aggregation in Azure Confidential Computing enclaves, convergence monitoring, non-IID handling (FedProx/SCAFFOLD), and cross-organization collaboration protocols.
Architecture
| Component | Technology | Purpose |
|---|---|---|
| Fed Server | Flower (flwr) + Container Apps | Round orchestration, aggregation, convergence |
| Client Training | PyTorch + Opacus DP | Local training on private data |
| Secure Aggregation | Azure Confidential Computing (SGX) | Hardware-enclave aggregation (optional) |
| Training Orchestration | Azure ML Workspace | Experiment tracking, compute management |
| Communication | TLS 1.3 | Encrypted clientβserver |
| Secrets | Azure Key Vault | API keys, enclave attestation |
ποΈ Full architecture details
How It Differs from Related Plays
| Aspect | Play 13 (Fine-Tuning) | Play 62 (Federated Learning) | Play 47 (Synthetic Data) |
|---|---|---|---|
| Data | Centralized training data | Data stays at client nodes (never centralized) | Generated from scratch |
| Privacy | Access controls | Differential privacy (Ξ΅ budget) | Privacy by construction |
| Training | Single-site | Multi-site distributed | N/A (generation, not training) |
| Output | Fine-tuned model | Global federated model | Synthetic dataset |
| Parties | Single organization | Multi-organization collaboration | Single organization |
| Compliance | Data handling policies | GDPR/HIPAA data sovereignty | GDPR synthetic data |
Key Metrics
| Metric | Target | Description |
|---|---|---|
| Rounds to Convergence | < 50 | Training rounds until stable loss |
| Global Accuracy | > 85% | Model accuracy on server test set |
| Accuracy vs Centralized | > 0.95 ratio | Federated nearly matches centralized |
| Epsilon Consumed | < budget | Total DP budget used |
| Data Isolation | 100% | No raw data transferred (non-negotiable) |
| Training Cost | Minimize | Client compute Γ rounds Γ clients |
Cost Estimate
| Service | Dev | Prod | Enterprise |
|---|---|---|---|
| Azure Machine Learning | $0 | $450 | $1,200 |
| Confidential Computing | $120 | $550 | $1,800 |
| Blob Storage | $5 | $40 | $120 |
| Container Apps | $15 | $200 | $600 |
| Key Vault | $1 | $15 | $40 |
| Virtual Network | $30 | $150 | $450 |
| Application Insights | $0 | $30 | $100 |
| Log Analytics | $0 | $20 | $60 |
| Total | $171/mo | $1,455/mo | $4,370/mo |
Estimates based on Azure retail pricing. Actual costs vary by region, usage, and enterprise agreements.
π° Full cost breakdown
WAF Alignment
| Pillar | Implementation |
|---|---|
| Security | Data never leaves client, DP noise, Confidential Computing, TLS 1.3 |
| Responsible AI | Privacy-preserving training, gradient leakage prevention |
| Reliability | Convergence monitoring, non-IID handling, early stopping |
| Cost Optimization | Client selection, early stopping, model compression |
| Operational Excellence | Round tracking, client contribution monitoring, LLM convergence explanation |
| Performance Efficiency | Async training, FedProx for non-IID, straggler mitigation |
FAI Manifest
| Field | Value |
|---|---|
| Play | 62-federated-learning-pipeline |
| Version | 1.0.0 |
| Knowledge | T1-Fine-Tuning-MLOps, T2-Responsible-AI, T3-Production-Patterns, F1-GenAI-Foundations, O5-GPU-Infra |
| WAF Pillars | security, responsible-ai, reliability, cost-optimization, performance-efficiency |
| Groundedness | β₯ 85% |
| Safety | 0 violations max |
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