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I have some exposure to federated learning and continual learning which are non-iid learning instances [1] and [2] I was wondering can we state the following:

Federated learning is when the dataset is distributed in a non-iid manner over space (different edge-devices at different geographical locations). Meanwhile, continual/incremental learning is when the dataset is distributed in a non-iid manner over time (each time a class is seen). Then, we could state that federated learning and continual learning are subclasses of non-iid learning.

Do you agree with this conclusion? What are the implications in your opinion?

kjetil b halvorsen
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    I don't think there is a universal/accepted classification. There are a lot of overlaps among the concepts: Federated learning implies privacy and distributed learning and can be practiced in many ways but sequential learning is not a prerequisite – msuzen Nov 27 '21 at 20:22

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