Advanced Topics
Federated Learning — Training Models Without Sharing Data
Learn how federated learning enables collaborative model training while keeping data private and secure. Essential for healthcare, finance, and privacy-sensitive applications.
- Federated Averaging — The core algorithm for distributed training
- Privacy Preservation — Keeping data local while learning globally
- Communication Efficiency — Reducing the cost of distributed learning
"The future of AI is decentralized and privacy-preserving."
Federated Learning — Complete Guide
Federated learning trains models across decentralized devices without centralizing data. Essential for privacy-sensitive applications.
Federated Learning Architecture
How It Works
The FedAvg Algorithm
Differential Privacy in Federated Learning
Communication Efficiency
Privacy-Utility Trade-off
Secure Aggregation
Protocol Overview:
- Pairwise Masking: Each pair of clients shares a random mask via Diffie-Hellman key exchange
- Summation: Each client sends to server
- Cancellation: Server sums all masked updates:
- Privacy: Individual updates remain hidden even from the server
Non-IID Data Challenges
Mitigation Strategies:
- FedProx: Add proximal term to local objective — keeps clients close to global model
- SCAFFOLD: Use control variates to correct client drift
- Per-Layer Fine-Tuning: Only aggregate certain layers, freeze others
- Data Augmentation: Synthetically balance data across clients
Federated Learning at Scale
Key Takeaways
What to Learn Next
-> ML Ethics — Fairness, Bias, Interpretability and Responsible AI Learn about ml ethics — fairness, bias, interpretability and responsible ai.
-> MLOps — Machine Learning Operations Complete Guide Learn about mlops — machine learning operations complete guide.
-> ML System Design — Architecture and Production Patterns Learn about ml system design — architecture and production patterns.
-> Model Evaluation — Metrics, Cross-Validation and Selection Learn about model evaluation — metrics, cross-validation and selection.
-> Model Deployment — APIs, Containers and Production ML Learn about model deployment — apis, containers and production ml.
-> Causal Inference — Moving Beyond Correlation Learn about causal inference — moving beyond correlation.