Editing Federated Learning (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a production federated learning system:

'''1. Threat model and privacy specification'''
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Who might be adversarial? (honest-but-curious server? malicious clients?)
    ↓
What level of privacy is required? (DP with ε ≤ 8? Secure aggregation?)
    ↓
What is the regulatory framework? (HIPAA, GDPR, CCPA?)
    ↓
Define privacy budget and acceptable accuracy-privacy trade-off
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'''2. System architecture'''
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[Orchestration server]
    ↓ (global model broadcast)
[Client selection algorithm]
    ↓ (based on data quality, staleness, fairness)
[Secure aggregation protocol] ← Clients send encrypted shares
    ↓
[Differential privacy: add calibrated Gaussian noise to aggregate]
    ↓
[Global model update]
    ↓
[Evaluation on held-out global validation set]
    ↓
[Anomaly detection: flag outlier client updates]
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'''3. Personalization strategies for non-IID data'''
* FedProx: penalize deviation from global model during local training
* Per-FedAvg: meta-learning approach; global model as a good initialization for local fine-tuning
* Clustered FL: group clients by data similarity, train separate global models per cluster
* Local fine-tuning: ship global model → each client fine-tunes for a few steps on local data

'''4. Infrastructure for cross-silo FL'''
* Dedicated FL node at each institution behind their firewall
* Encrypted communication channels (mTLS)
* Audit logs for every gradient exchange
* Governance contract specifying data usage, participation requirements, IP ownership

[[Category:Artificial Intelligence]]
[[Category:Machine Learning]]
[[Category:Privacy]]
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