Glossary

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Federated Learning(also: FL)

A machine-learning approach in which a shared model is trained across many user devices without the raw training data ever leaving those devices: each device computes updates locally and sends only model parameters or gradients to a central server for aggregation. Federated…

Few-Shot Object Recognition(also: Few-Shot Recognition)

A machine learning approach in which a model learns to identify a novel object from only a handful of labelled examples (commonly one to ten) rather than the hundreds or thousands typical of conventional supervised training. Few-shot object recognition underpins teachable and…

Fine-tuning(also: Model Fine-tuning, Fine-tune, Supervised Fine-tuning)

A machine-learning technique that adapts a pre-trained foundation model - typically a large language model or vision model - to a specific task, domain, or individual user by continuing training on a smaller, targeted dataset. Fine-tuning preserves the broad capabilities of the…

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