Non-iid data refers to data that does not follow the independent and identically distributed (iid) assumption, meaning the samples are not statistically independent and may come from different distributions. In the context of federated learning and privacy-preserving deep learning, this type of data is common as it reflects real-world scenarios where data collected from different users or devices can vary significantly in quality, distribution, and structure.
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