Foundations · MLD-0426
Federated learning
Training across separate data holders while coordinating model updates instead of centralizing raw data.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Train across devices with local datasets.
Original caveat
Federation alone does not guarantee privacy.
Context and aliases
Level: Foundational. Stages: Theory · Training · Inference.
Tags: ML · DL · SLM · LLM.
Aliases: None recorded.
Related concepts
Technology connections
- Firecracker — Lightweight virtual-machine monitor for microVMs.
- Flower — Federated learning framework.
- gVisor — Sandboxed container runtime with a user-space kernel boundary.
- Microsoft Presidio — Toolkit for detecting and anonymizing sensitive information.
- NVIDIA FLARE — Framework for federated learning and analytics.
- Opacus — Differentially private training library for PyTorch.
- Open Policy Agent — General-purpose policy engine.
- TensorFlow Privacy — Differential privacy tools for TensorFlow.
Original references
- Deep Learning: online textbook ↗ · Consulted
- Google Machine Learning Glossary ↗ · Consulted
- Original reference URL ↗