ML Fundamentals
Federated Learning
Training an AI model across many devices without ever collecting the raw data in one place, privacy by design.
Definition
A machine learning technique where a model is trained across multiple decentralized devices or servers holding local data samples, without exchanging raw data. Only model updates (gradients) are shared.
Why it matters
Enables AI training on sensitive data (medical records, financial data) without compromising privacy.
Related terms in ML Fundamentals
Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
From vocabulary to outcomes
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