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Transfer Learning and Federated Learning
When we discuss deep learning in the real world, we quickly run into two practical constraints: data and privacy. Some tasks suffer from a lack of labeled data, making it hard to train a strong model from scratch. Others deal with sensitive information, such as medical scans, financial records, and personal behavior data, which cannot be transferred freely to centralized servers.
Transfer Learning and Federated Learning emerged from these two needs. They are now essential techniques for any AI practitioner, whether in industry or research. Let’s walk through both, not in a rigid point-form note style, but in clear, connected paragraphs — while keeping point-form only where it sharpens the explanation.
Understanding Transfer Learning
A good way to understand transfer learning is to imagine you’re learning to play a new instrument. If you already know the piano, switching to the guitar will still require practice, but your sense of rhythm, scales, and musical structure carries over. Similarly, deep neural networks trained on large datasets learn general patterns that are useful far beyond the original task.
Suppose you want to classify MRI images, but you only have a small medical dataset. Meanwhile, there’s ImageNet, with millions of pictures of everyday objects. The two tasks may seem unrelated on the surface, yet the early layers of a CNN trained on ImageNet learn fundamental patterns — edges, curves, textures. These are universal visual…
